Modeling Academic Performance Evaluation using Fuzzy C-Means Clustering Techniques

Save this PDF as:

Size: px
Start display at page:

Transcription

3 center points and summed. Finally, this area is divided by the sum of the weighted member function strengths and the result is taken as the crisp output. 3. DATA CLUSTER ANALYSIS FOR ACADEMIC PERFORMANCE EVALUATION The clustering problem can be stated simply as follows: Given a finite set of data, X, develop a grouping scheme for grouping the objects into classes. In classical cluster analysis, these classes are required to form a partition of X such that the degree of association is strong for data within blocks of the partition and weak for data in different blocks. However, this requirement is too strong in many practical applications, and it is thus desirable to replace it with a weaker requirement. When the requirement of a crisp partition of X is replaced with a weaker requirement of a fuzzy partition or a fuzzy pseudo partition on X, we refer to the emerging problem area as fuzzy clustering. Fuzzy pseudo partitions are often called fuzzy c-partitions, where c designates the number of fuzzy classes in the partition [21]. Pattern recognition techniques can be classified into two broad categories: unsupervised techniques and supervise techniques. An unsupervised technique does not use a given set of unclassified data, whereas a supervised technique uses a dataset with known classification. These two types of techniques are complementary to each other. The Hard C-Means and Fuzzy C- Means clustering techniques fall in unsupervised category. In this paper, we have used Fuzzy C-Means clustering techniques for students academic performance evaluation. 4. FUZZY C-MEANS (FCM) CLUSTERING TECHNIQUE The fuzzy C-Means algorithm (FCM) generalizes the hard C- Means algorithm to allow a point to partially belong to multiple clusters. Therefore, it produces a soft partition for a given dataset. In fact, it produces a constrained soft partition [22]. To this, the objective function J 1 of hard C-Means has been extended in two ways: 1. The fuzzy membership degrees in clusters were incorporated into the formula. 2. An additional parameter m was introduced as a weight exponent in the fuzzy membership. The extended objective function, denoted J m, is Where P is a fuzzy partition of the dataset X formed by. The parameter m is a weight that determines the degree to which partial members of a cluster affect the clustering result. Like hard c-means, fuzzy c-means also tries to find a good partition by searching for prototypes v i that minimize the objective function J m. Unlike hard C-means, however, the fuzzy C-means algorithms also need to search for membership functions that minimize J m. To accomplish these two objectives, necessary conditions for local minimum of J m was derived from J m are given below in theorem 4.1. The fuzzy C-means (FCM) algorithm is given below: FCM(X, c, m, ) X : An unlabeled data set C : the number of clusters to form m : the parameter in the objective function : A threshold for the convergence criteria Initialize prototype Repeat Compute membership function using equation (2). Update the prototype, v i in V using equation (3). Until (1) Until convergence criteria is met Fuzzy C-Means Theorem A constrained fuzzy partition can be a local minimum of the objective function Jm only if the following conditions are satisfied: (2) Bases on this theorem, FCM updates the prototypes and the membership function iteratively using equation (2) and (3) until a convergence criterion is reached. We describe the algorithm in section REGRESSION MODEL Regression is one of the most common problems in statistics. It consists in exploring the association between dependent and independent variables and in identifying their impact on the dependent variable. Ordinarily, we do not have knowledge of the exact functional relationship between the two random variables x and y, where to each vector x sampled according to a distribution P(x) there corresponds a scalar in accordance to a conditional distribution P(y/x). Typically we proceed by assuming that the target variables y is given by some deterministic function of x with added Gaussian noise that represents a measurement error or, more generally, our ignorance about the dependence of y on x (H. White, 1989)[34]: (4) The function is called the regression function and the statistical model described by the above equation is called regression model. The error is a random variable having a normal distribution with zero mean, and a standard deviation which does not depend on x or y, that is: This common assumption can be partly justified by results from experimental measurements and by the central limit theorem, which states that the sample mean of any reasonable distribution can be approximated by a normal distribution. It follows from this assumption and from (4) that the conditional distribution of y given x will be a normal distribution with mean and variance. Hence we obtain: (6) That is is the conditional mean of the output y given the input x. In other words, the regression of y on x is that (deterministic) function of x that gives the mean value of y conditional on x. It can be demonstrated that the regression function is an excellent solution to the problem of fitting the data, i.e. among all functions of x, the regression is the best predictor of y given x, in the squared-error sense. Precisely, it can be shown that the minimum of the risk functional: (7) Is attained by the regression function. Thus the problem of regression estimation can be addressed in the statistical learning framework, once the learning machine is assessed by a quadratic loss function: (8) In the case of a quadratic loss function, the empirical risk functional becomes: (3) (5) (9) 17

4 Which is usually referred to as the Mean Squared Error (MSE)? This regression model is used to estimate the output of proposed rule based Fuzzy Expert System. 6. ARCHITECTURE OF PROPOSED RULE BASED FUZZY EXPERT SYSTEM In this paper, we have proposed rule based Fuzzy Expert System for student academic performance evaluation. The proposed rule based Fuzzy Expert System consists of Fuzzy Logic, Fuzzy C- means clustering algorithm and Regression analysis model. The Fuzzy C-Means clustering algorithm is used for classify input space into different classes or clusters and regression analysis model used for output estimation of the input data Rule Based Fuzzy Expert System The world of information is surrounded by uncertainty and imprecision. The human reasoning process can handle inexact, uncertain, and vague concepts in an appropriate manner. Usually, the human thinking, reasoning, and perception process cannot be expressed precisely. These types of experiences can rarely express or measured using statistical or probability theory. Fuzzy logic provides a framework to model uncertainty, the human way of thinking, reasoning, and the perception process. Fuzzy system was introduced by Zadeh [3]. A fuzzy expert system is simply an expert system that uses a collection of fuzzy membership functions and rules, instead of Boolean logic, to reason about data [36]. The rules in a fuzzy expert system are usually of a form similar to the following: If A is Low and B is High then (X = Medium). Where A and B are input variables, X is an output variable. Here low, high and medium are fuzzy sets defined on A, B and X respectively. The antecedent (the rule s premise) describes to what degree the rule applies, while the rule s consequent assigns a membership function to each of one or more output variables. Let X is a space of objects and x be a generic element of X. A classical set, is defined as a collection of elements objects, such that x can either belong or not belong to the set. A Fuzzy set A in X is defined as a set of ordered pairs:, where is called the membership function (MF) for the fuzzy set A. The MF maps each element of X to a membership grade (or membership value) between zero and one. Figure-2 shows the basic architecture of proposed rule based Fuzzy Expert System for academic performance evaluation. Crisp Input Fuzzification Inference Fuzzy Input Inference Engine Fuzzy Output Defuzzification Inference Rules Crisp Output Fuzzy Rule Base Figure-2: Architecture of Proposed Rule Based Fuzzy Expert System The main components of proposed rule based fuzzy expert system are: a fuzzification interface, a fuzzy rule-base (knowledge base), an inference engine (decision making logic), and a defuzzification interface [35]. (i) Fuzzification Interface: The input variables are fuzzified by the Fuzzy C-Means clustering algorithm. (ii) Fuzzy Rule Base (Knowledge Base): Fuzzy if-then rules and fuzzy reasoning are the backbone of fuzzy expert systems, which are the most important modeling tools based on fuzzy set theory. The rule base is characterized in the form of if-then rules in which the antecedents and consequents involve linguistic variables. In this paper, we use very high, high, average, low and very low as linguistic variable. The collection of these rules forms the rule base for the fuzzy logic system. In this proposed rule based fuzzy expert system, we have used the following rules for finding the knowledge base: 1. If student belong to very high then 2. If student belong to high then 3. If student belong to average then 4. If student belong to low then 5. If student belong to very low then Where X is the students mark obtained in semester-1 examination. are constant determine by the method of regression analysis model. (iii) Inference Engine (Decision Making Logic): Using suitable inference procedure, the truth value for the antecedent of each rule is computed and applied to the consequent part of each rule. Here, we have used the regression analysis model for decision making. This results in one fuzzy subset to be assigned to each output variable for each rule. Again, by using suitable composition procedure, all the fuzzy subsets to be assigned to each output variable are combined together to form a single fuzzy subset for each output variable. (iv) Defuzzification Interface: Defuzzification means convert fuzzy output into crisp output. Here, we have used the height defuzzification technique for converting fuzzy output into crisp output (performance value of students). The defuzzification formula (Takagi-Sugeno-Kang Model) is given below: With the help of this equation, we can convert the fuzzy output into crisp output (performance value of a student). 7. EXPERIMENTAL RESULTS OF PROPOSED RULE BASED FUZZY EXPERT SYSTEM In this paper, we have proposed a method called rule based Fuzzy Expert System for academic performance evaluation. We consider here a method by which fuzzy membership function may be created for fuzzy classes of an input data set by using Fuzzy C-Means clustering algorithms. Let us consider, 20 students marks obtained by Semester-1 and Semester-2 examination. Table-1 shows the scores achieved by 20 s B.Tech. 2 nd year students in the Department of Computer Science and Engineering, Ashoka Institute of Technology and Management, Saranath, Varanasi , Uttar Pradesh, India, appeared in semester-i and semester-ii examination. Here, we 18

5 use the MATLAB software for modeling students academic performance evaluation. Table 1. Data Set of Students Score in Sem.-1 and Sem.-2 S.No. Sem.-1 Sem.-2 S.No. Sem.-1 Sem The above data points (Table-4) are first divided into different clusters using Fuzzy C-Means Clustering Techniques. The steps of proposed method are given below: Step-1 (Fuzzification): Here, we have used Fuzzy C-Means clustering Algorithms for classifying students scores data set (conversion of crisp score into fuzzy set), given in Table-1. For this purpose, we have used MATLAB software for classifying (Clustering) the students data score into five classes or Clusters, namely Very High, High, Average, Low, and Very Low for modeling students academic performance evaluation, shown in Table-2. Figue-2 shows the students dataset partitioned into five classes or clusters. Figue-4 shows the performance of objective function for students academic performance evaluation. Table 3 gives the cluster centers of Very High, High, Average, Low and Very Low. Table-2. The membership functions for fuzzy clustering of Students Academic Performance Evaluation by Fuzzy C-Means Algorithms S.No. Sem.-1 Sem.-2 Fuzzy C-Means Clustering Technique Very High (VH) High (H) Average (A) Low (L) Very Low (VL) Table 3. The cluster centers of Very High, High, Average, Low and Very Low S.No. Cluster Center Sem.-1 Sem Cluster Centre of Very High Cluster Centre of High Cluster Centre of Average Cluster Centre of Low Cluster Centre of Very Low The component value of vectors P and V are obtained by soling the fuzzy clustering problem (Academic Performance Evaluation problem), which is basically constrained optimization problems in equation (1). A description of each item of notation as follows: 1. The variable k represents the number of students sit in Semester-1 and Semester-2, who will be allocated into C classes or clusters. 2. The variable C represents the number of classes or clusters, the value of this variable can be determined by the institution policy. 3. The matrix consists of n rows and c columns, of which the element represents the degree of membership (or the suitability level) of the k th student. 4. The matrix, consists of m rows and c columns, of which the element represents the (weighted) average of students grade achieved by students, belong to the cluster (or class). 5. In extreme condition, the value of the fundamental equation (10) is 0, which indicates the obtained clusters are ideal, since they consist of students with the same level of mastery. Principally, the minimum the value of is, then the better the clustering process. The application of fuzzy C-Means Algorithm (FCM) illustrated by a case described as dataset of students score marks shown in Table-6. Table-6 gives the value of elements of vector U i (i=1, 2, 3). As an illustration, the values in the 11 th row of Table-6 can be interpreted as: 19

6 Very High = , High = , Average = , Low = , Very Low = Max = (0.0192, , , , = From those five values, 11 th student is the most suitable to be in class or cluster (Low), since he/she has the highest degree of membership to this class or cluster compared to the other four. 5 th student is the most suitable to be in class or cluster (average), since he/she has the highest degree of membership to this class or cluster compared to the other four. Thus, we conclude that 5 th student has improved consistently while 11 th student has deteriorated consistently. By the same observations, the following class or cluster was obtained for students partitioning in Semester-1 and Semester-2 examinations: 1. The first class or cluster (Very High) consists of students numbers 12, and The second class or cluster (High) consists of students numbers 8, 9, 18 and The third class or cluster (Average) consists of students numbers 1, 3, 5, 6, 7, 10, 15, and The fourth class or cluster (Low) consists of students numbers 11, 14 and The fifth class or cluster (Very Low) consists of students numbers 2, 4 and 20. Thus, two students belong to class or cluster (Very High), four students belong to class or cluster (High), eight students belong to class or cluster (Average), three students belong to class cluster (Low) and three students belong to class or cluster (Very Low). Figure-3: Partition of the students score dataset for academic performance evaluation Step-2 (Output Estimation): Regression problems deal with estimation of an output value based on input values. When used for classification, the input values are values from the database and the output values represents the classes. Regression can be used to solve classification problems. In actually, regression takes a set of data and fits the data to formal. The linear regression formula in two dimensional spaces is given bellow: (10) Where a and b are constant. They are determining by the normal equations for best fit of linear relationship of input and output. This model is estimate the actual relationship between input and output. We can use the generated linear regression model to predict an output value given an input value. Here, we use the regression analysis of output estimation of rule based Fuzzy Expert System for modeling academic performance evaluation. In this proposed research work, we have used linear regression model for estimation of output of rule based Fuzzy Expert System. Here we have used the MATAB software for estimating the output of DFES. The output of cluster (Very High), cluster (High), Cluster (Average), cluster (Low) and Cluster (Very Low) are given bellow: Where X is students mark of semester-1. Step-3 (Rule Generation): 1. If Student belongs to cluster (very high) then student performance is very high. 2. If student is belongs to cluster (high) then student performance is high ). 3. If student is belongs to cluster (average) then student performance is average( 4. If student belongs to cluster (low) then student performance low. 5. If student belongs to cluster very low then student performance is very low (. If we take the first student of Table-6, then the output of Y is given by: Very High: Y = 100, High: Y = *40 = , Average: Y = *40 = , Low: Y = *40 = 2.5, Very Low: Y = = Step-4 (Defuzzification) Calculation of Academic Performance The final calculation of student academic performance is determined by the following formula: Average Low Similarly, we can calculate the academic performance of other students given in Table-4. 20

7 Figure-4: Performance of Objective Function Table-4: The membership functions and Students Academic Performance Calculated by the Rule Based Fuzzy Expert System S.No. Sem.-1 Sem.-2 Student Performance using Rule Based Fuzzy Expert System Very High (VH) High (H) Average (A) Low (L) Very Low (VL) Student Performance (SP) From above Table-4 shows that the 11 th student is the most suitable to be in class or cluster (Low), since this student has the highest degree of membership to this class or cluster compared to the other four. 5 th student is the most suitable to be in class or cluster (average), since this student has the highest degree of membership to this class or cluster compared to the other four. Thus, we conclude that 5 th student has improved consistently while 11 th student has deteriorated consistently. Therefore, the fuzzy C-Means clustering technique method is more suitable than the classical method for academic performance evaluation. In this model, the numbers of fuzzy rules are very less in comparison to existing classical Fuzzy Expert System. Therefore, the proposed rule based Fuzzy Expert System is more efficient for computational point of view. The proposed rule based Fuzzy Expert System also calculate the total marks of a particular student. For example, 1 st student has secured , 2 nd student has secured and 3 rd student has secured etc. 21

Summary, Conclusion and Future Perspectives

Chapter 6 Summary, Conclusion and Future Perspectives 6.1 Summary and Conclusions The work presented in this thesis belongs to the framework of Fuzzy Logic Control Technique versus Classical mathematical

[Ayuba*, 4.(6): June, 2015] ISSN: (I2OR), Publication Impact Factor: 3.785

IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY THE APPLICATION OF FUZZY LOGIC IN ADMITTING STUDENTS INTO TERTIARY INSTITUTIONS OF LEARNING Peter Ayuba*, Tella Yohanna, Sa adatu

Chapter- 6 : Machine Learning - Machine learning is a branch of AI that uses algorithm to allow computer to evolve behaviors based on data collected from databases or gathered through sensors. - Machine

Improvement of Text Summarization using Fuzzy Logic Based Method

IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727 Volume 5, Issue 6 (Sep-Oct. 2012), PP 05-10 Improvement of Text Summarization using Fuzzy Logic Based Method 1 Rucha S. Dixit,

An Introduction of Soft Computing Approach over Hard Computing

An Introduction of Soft Computing Approach over Hard Computing Puja Gupta Department of Computer Engineering S.G.S.I.T.S. Indore, M.P, India Neha Kulkarni Department of Computer Engineering S.G.S.I.T.S.

Automatic Generation of Fuzzy Models By using Iteration through Training Data

Automatic Generation of Fuzzy Models By using Iteration through Training Data Abdulrazaq Alsuhail Almutairi Information & Computer Center The Public Authority for Applied Education and Training The Ministry

Teacher s Performance Appraisal System Using Fuzzy Logic- A Case Study

Teacher s Performance Appraisal System Using Fuzzy Logic- A Case Study G.Vasanti The Department of Basic Science and Humanities Aditya Institute of technology and management, Tekkali, Srikakulam(dist-532003,

Fuzzy Inference Sytem for Teaching Staff Performance Appraisal

Fuzzy Inference Sytem for Teaching Staff Performance Appraisal G.A.Bhosale Department of Computer Studies Chh. Shahu Institute of Business Education and Research Kolhapur, India R. S. Kamath * Department

Prediction of Student s Academic Performance using Clustering

Cloud Computing & Big Data 1 Prediction of Student s Academic Performance using Clustering Prof. Prashant Sahai Saxena Joint Director, School of Computer and Systems Sciences Jaipur National University,

A student diagnosing and evaluation system for laboratory-based academic exercises

A student diagnosing and evaluation system for laboratory-based academic exercises Maria Samarakou, Emmanouil Fylladitakis and Pantelis Prentakis Technological Educational Institute (T.E.I.) of Athens

Use of Neural Networks for Data Mining in Official Statistics

Use of Neural Networks for Data Mining in Official Statistics Jana Juriová 1 1 Institute of Informatics and Statistics (INFOSTAT), e-mail: juriova@infostat.sk Abstract One of the main challenges raised

APPLICATION OF FUZZY LOGIC IN IDENTIFICATION OF GIFTED STUDENTS

APPLICATION OF FUZZY LOGIC IN IDENTIFICATION OF GIFTED STUDENTS 1 Introduction and description of reseach problem Defining giftedness, it is most often described as an individual s ability, which is quantitatively

SOFTCOMPUTING IN MODELING & SIMULATION

SOFTCOMPUTING IN MODELING & SIMULATION 9th July, 2002 Faculty of Science, Philadelphia University Dr. Kasim M. Al-Aubidy Computer & Software Eng. Dept. Philadelphia University The only way not to succeed

Lecture 1: Machine Learning Basics

1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

TUNING A FUZZY EXPERT SYSTEM FOR SIMULATIONS OF POPULATION BEHAVIOR

Proceedings of the IASTED International Conference Modeling and Simulation (MS 99) May 5-8, 1999, Philadelphia, Pennsylvania - USA TUNING A FUZZY EXPERT SYSTEM FOR SIMULATIONS OF POPULATION BEHAVIOR CAROLYN

Efficient Document Clustering System Based on Probability Distribution of K-Means (PD K-Means) Model

Efficient Document Clustering System Based on Probability Distribution of K-Means (PD K-Means) Model Tin Thu Zar Win 1, Nang Aye Aye Htwe 2, Department of Computer Engineering and Information Technology,

STA 414/2104 Statistical Methods for Machine Learning and Data Mining

STA 414/2104 Statistical Methods for Machine Learning and Data Mining Radford M. Neal, University of Toronto, 2014 Week 1 What are Machine Learning and Data Mining? Typical Machine Learning and Data Mining

Determining Factors Influencing Listening Test Item Difficulty and Predicting Reading Proficiency

Determining Factors Influencing Listening Test Item Difficulty and Predicting Reading Proficiency Vahid ARYADOUST Centre for English Language Communication, National University of Singapore 1 Outline Two

Applications of Fuzzy Logic and Artificial Neural Networks in Evaluation and Ranking of Teachers Based on Framework for Teaching Model

International Academic Institute for Science and Technology International Academic Journal of Innovative Research Vol. 3, No. 2, 2016, pp. 1-10. ISSN 2454-390X International Academic Journal of Innovative

Fall 2015 COMPUTER SCIENCES DEPARTMENT UNIVERSITY OF WISCONSIN MADISON PH.D. QUALIFYING EXAMINATION

Fall 2015 COMPUTER SCIENCES DEPARTMENT UNIVERSITY OF WISCONSIN MADISON PH.D. QUALIFYING EXAMINATION Artificial Intelligence Monday, September 21, 2015 GENERAL INSTRUCTIONS 1. This exam has 10 numbered

Enhanced Fuzzy System for Student s Academic Evaluation using Linguistic Hedges

Enhanced Fuzzy System for Student s Academic Evaluation using Linguistic Hedges Ibrahim A. Hameed, Senior Member IEEE Dept. of Information and Communication Technology (ICT) and Natural Sciences Faculty

Learning of Open-Loop Neural Networks with Improved Error Backpropagation Methods

Learning of Open-Loop Neural Networks with Improved Error Backpropagation Methods J. Pihler Abstract The paper describes learning of artificial neural networks with improved error backpropagation methods.

This chapter summarizes the results and contributions of this dissertation.

99 CHAPTER 8 CONCLUSION This chapter summarizes the results and contributions of this dissertation. The conclusions detailing the overall implications of the methodologies introduced in this dissertation

A Fuzzy Multi Objective Approach to Waste Management

A Fuzzy Multi Objective Approach to Waste Management Department of Applied Mathematics and Computational Sc. Shri G.S.Institute of Technology & Science,Indore,Madhya Pradesh,India Abstract: Nowadays, waste

Keywords- ANP, Multi-Criteria Decision Making (MCDM), Fuzzy theory, FANP, Supermatrix.

Volume 3, Issue 5, May 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Fuzzy Theory Concept

An Evaluation of Faculty Performance in Teaching Using Fuzzy Modeling Approach

An Evaluation of Faculty Performance in Teaching Using Fuzzy Modeling Approach Bhavika Tailor 1, Rasik Shah 2, Dr. Jayesh Dhodiya 3, Dr. Dilip Joshi 4 1 ASH Department, C.G.Patel Institute of Technology,

Progress Report (Nov04-Oct 05)

Progress Report (Nov04-Oct 05) Project Title: Modeling, Classification and Fault Detection of Sensors using Intelligent Methods Principal Investigator Prem K Kalra Department of Electrical Engineering,

INSTITUTE OF AERONAUTICAL ENGINEERING (Autonomous) Dundigal, Hyderabad ELECTRICAL AND ELECTRONICS ENGINEERING

INSTITUTE OF AERONAUTICAL ENGINEERING (Autonomous) Dundigal, Hyderabad - 500 043 ELECTRICAL AND ELECTRONICS ENGINEERING QUESTION BANK Course Name : NEURAL NETWORKS AND FUZZY LOGIC Course Code : 58009 Class

Fuzzy Logic Method for Evaluation of Difficulty Level of Exam and Student Graduation

IJCSI International Journal of Computer Science Issues, Vol., Issue 2, No 2, March 13 www.ijcsi.org 223 Fuzzy Logic Method for Evaluation of Difficulty Level of Exam and Student Graduation Rusmiari 1,

SCIENCE & TECHNOLOGY

Pertanika J. Sci. & Technol. 25 (2): 619-630 (2017) SCIENCE & TECHNOLOGY Journal homepage: http://www.pertanika.upm.edu.my/ Review of Context-Based Similarity for Categorical Data Nurul Adzlyana, M. S.*,

A Neuro-Fuzzy Synergism to Intelligent Systems. For book and bookstore information.

Neural Fuzzy Systems A Neuro-Fuzzy Synergism to Intelligent Systems Chin-Teng Lin Department of Control Engineering National Chiao-Tung University Hsinchu, Taiwan C.S.George Lee School of Electrical and

Use of Data Mining & Neural Network in Medical Industry

Current Development in Artificial Intelligence. ISSN 0976-5832 Volume 3, Number 1 (2012), pp. 1-8 International Research Publication House http://www.irphouse.com Use of Data Mining & Neural Network in

A Review on Classification Techniques in Machine Learning

A Review on Classification Techniques in Machine Learning R. Vijaya Kumar Reddy 1, Dr. U. Ravi Babu 2 1 Research Scholar, Dept. of. CSE, Acharya Nagarjuna University, Guntur, (India) 2 Principal, DRK College

How well do people learn? Classifying the Quality of Learning Based on Gaze Data

How well do people learn? Classifying the Quality of Learning Based on Gaze Data Bertrand Schneider Stanford University schneibe@stanford.edu Yuanyuan Pao Stanford University ypao@stanford.edu ABSTRACT

Unsupervised Learning

09s1: COMP9417 Machine Learning and Data Mining Unsupervised Learning June 3, 2009 Acknowledgement: Material derived from slides for the book Machine Learning, Tom M. Mitchell, McGraw-Hill, 1997 http://www-2.cs.cmu.edu/~tom/mlbook.html

Educational Data Mining for Teaching and Learning. Zhi-Jun PEI 1,a

2017 2nd International Conference on Education and Development (ICED 2017) ISBN: 978-1-60595-487-5 Educational Data Mining for Teaching and Learning Zhi-Jun PEI 1,a 1 School of Electronic Engineering,

Text Classification with Machine Learning Algorithms

2013, TextRoad Publication ISSN 2090-4304 Journal of Basic and Applied Scientific Research www.textroad.com Text Classification with Machine Learning Algorithms Nasim VasfiSisi 1 and Mohammad Reza Feizi

ENHANCING FUZZY INFERENCE SYSTEM BASED CRITERION-REFERENCED ASSESSMENT WITH AN APPLICATION

ENHANCING FUZZY INFERENCE SYSTEM BASED CRITERION-REFERENCED ASSESSMENT WITH AN APPLICATION Kai Meng Tay Chee Peng Lim Electronic Engineering Department School of Electrical & Electronic Engineering Faculty

International Journal of Advance Research in Engineering, Science & Technology I. INTRODUCTION

Impact Factor (SJIF): 5.301 International Journal of Advance Research in Engineering, Science & Technology e-issn: 2393-9877, p-issn: 2394-2444 Volume 6, Issue 4, April-2019 Performance analysis based

International Journal of Scientific & Engineering Research, Volume 5, Issue 6, June-2014 ISSN

International Journal of Scientific & Engineering Research, Volume 5, Issue 6, June-2014 198 Analyzing the Student s Academic Performance by using Clustering Methods in Data Mining Sreedevi Kadiyala, Chandra

CHAPTER 4 IMPROVING THE PERFORMANCE OF A CLASSIFIER USING UNIQUE FEATURES

38 CHAPTER 4 IMPROVING THE PERFORMANCE OF A CLASSIFIER USING UNIQUE FEATURES 4.1 INTRODUCTION In classification tasks, the error rate is proportional to the commonality among classes. Conventional GMM

Artificial Intelligence Introduction to Machine Learning

Artificial Intelligence Introduction to Machine Learning Artificial Intelligence Chung-Ang University Narration: Prof. Jaesung Lee Introduction Applications which Machine Learning techniques play an important

Chapter -2 Artificial Neural Network

Chapter -2 Artificial Neural Network 2.1 Introduction Artificial Neural Network is inspired by the neuron structure of human brain. The brain learns from experiences and adapts accordingly, which is beyond

Software Defect Prediction using Support Vector Machine

ISSN: 2454-132X Impact factor: 4.295 (Volume3, Issue1) Available online at: www.ijariit.com Software Defect Prediction using Support Vector Machine Er. Ramandeep Kaur Bahra Group of Institutes, Patiala.

New Cluster Validation with Input-Output Causality for Context-Based Gk Fuzzy Clustering

New Cluster Validation with Input-Output Causality for Context-Based Gk Fuzzy Clustering Keun-Chang Kwak Dept. of Control and Instrumentation Engineering Chosun University, 375 Seosuk-Dong Gwangju, Korea

SOME INVESTIGATIONS IN FUZZY AUTOMATA SYNOPSIS

SOME INVESTIGATIONS IN FUZZY AUTOMATA SYNOPSIS Mathematical models in classical computation, automata have been an important area in theoretical computer science. It started from a seminal paper of Kleene,

THE FUZZY-NEURO CLASSIFIER FOR DECISION SUPPORT. Galina Setlak

International Journal "Information Theories & Applications" Vol.15 / 2008 21 THE FUZZY-NEURO CLASSIFIER FOR DECISION SUPPORT Galina Setlak Abstract: This paper aims at development of procedures and algorithms

to compare the performance of different classifiers obtained for different class distributions, the same test data is used.

The Effect of Imbalanced Data Class Distribution on Fuzzy Classifiers - Experimental Study Sofia Visa Department of ECECS, University of Cincinnati, Cincinnati, OH 4522-3, USA svisa@ececs.uc.edu Anca Ralescu

International Conference on Information Technology and Management Innovation (ICITMI 2015)

International Conference on Information Technology and Management Innovation (ICITMI 2015) Study of SF 6 circuit breaker fault diagnosis expert system Yi YUAN a, Zhen HUANG b and Hui YANG c Lanzhou University

Fuzzy Systems. Heuristic Fuzzy Rule Learning Approaches

Fuzzy Systems Heuristic Fuzzy Rule Learning Approaches Prof. Dr. Rudolf Kruse Christian Moewes {kruse,cmoewes}@iws.cs.uni-magdeburg.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science

Volgenau School of Engineering. Final Report of Project ECE

Volgenau School of Engineering Final Report of Project ECE 699-002 Title: Evaluation of Learning Algorithms on the Data of Self-Organizing Network to Select a Model for Predicting of the Next Call Blocking

The 2-Tuple Linguistic Representation Approach for Learning Competence Evaluation

The 2-Tuple Linguistic Representation Approach for Learning Competence Evaluation C 12 Sri Andayani Department of Mathematics Education Yogyakarta State University Indonesia andayani_uny@yahoo.com Abstract

On The Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis

On The Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis Asriyanti Indah Pratiwi, Adiwijaya Telkom University, Telekomunikasi Street No 1, Bandung 40257, Indonesia

The research of fuzzy decision trees building based on entropy and the theory of fuzzy sets

The research of fuzzy decision trees building based on entropy and the theory of fuzzy sets S B Begenova 1 and T V Avdeenko 1 1 Novosibirsk State Technical University, Karla Marks ave 20, Novosibirsk,

Unsupervised Learning: Clustering

Unsupervised Learning: Clustering Vibhav Gogate The University of Texas at Dallas Slides adapted from Carlos Guestrin, Dan Klein & Luke Zettlemoyer Machine Learning Supervised Learning Unsupervised Learning

Important properties of artificial neural networks will be discussed, namely that,

CP8206 Soft Computing & Machine Intelligence 1 PRINCIPLE OF ARTIFICIAL NEURAL NETWORKS Important properties of artificial neural networks will be discussed, namely that, (i) the underlying principle of

State of Machine Learning and Future of Machine Learning

State of Machine Learning and Future of Machine Learning (based on the vision of T.M. Mitchell) Rémi Gilleron Mostrare project Lille university and INRIA Futurs www.grappa.univ-lille3.fr/mostrare Collège

Lecture 2 Fundamentals of machine learning

Lecture 2 Fundamentals of machine learning Topics of this lecture Formulation of machine learning Taxonomy of learning algorithms Supervised, semi-supervised, and unsupervised learning Parametric and non-parametric

Ensemble Neural Networks Using Interval Neutrosophic Sets and Bagging

Ensemble Neural Networks Using Interval Neutrosophic Sets and Bagging Pawalai Kraipeerapun, Chun Che Fung and Kok Wai Wong School of Information Technology, Murdoch University, Australia Email: {p.kraipeerapun,

DATA WARE HOUSING AND MINING

Code No: RT32052 R13 SET - 1 III B. Tech II Semester Supplementary Examinations, November/December-2016 DATA WARE HOUSING AND MINING (Common to CSE and IT) Time: 3 hours Maximum Marks: 70 Note: 1. Question

IAI : Machine Learning

IAI : Machine Learning John A. Bullinaria, 2005 1. What is Machine Learning? 2. The Need for Learning 3. Learning in Neural and Evolutionary Systems 4. Problems Facing Expert Systems 5. Learning in Rule

Categorical Probability Proportion Difference (CPPD): A Feature Selection Method for Sentiment Classification

Categorical Probability Proportion Difference (CPPD): A Feature Selection Method for Sentiment Classification Basant Agarwal, Namita Mittal Department of Computer Engineering, Malaviya National Institute

Risk Status Prediction and Modelling Of Students Academic Achievement - A Fuzzy Logic Approach

Research Inventy: International Journal Of Engineering And Science Vol.3, Issue 11 (November 2013), PP 07-14 Issn(e): 2278-4721, Issn(p):2319-6483, www.researchinventy.com Risk Status Prediction and Modelling

Tapas Joshi Atefeh Mahdavi Chandan Patil. Semi-Supervised Learning with Ladder Networks CSE 5290 Artificial Intelligence

1. Introduction Semi-Supervised Learning with Ladder Networks CSE 5290 Artificial Intelligence Group 2 In this modern era of autonomous cars and deep learning, pure supervised learning is widely popular

Fuzzy Multicriteria Analysis for Student Project Evaluation

Fuzzy Multicriteria nalysis for Student Project Evaluation. Pejić *, P. M. Stanić **, Sz. Pletl **,. Kiss *** * Óbuda University, udapest, Hungary ** SuboticaTech/epartment of Informatics, Subotica, Serbia

Lecture 7: Distributed Representations

Lecture 7: Distributed Representations Roger Grosse 1 Introduction We ll take a break from derivatives and optimization, and look at a particular example of a neural net that we can train using backprop:

Ensemble Learning with Dynamic Ordered Pruning for Regression

Ensemble Learning with Dynamic Ordered Pruning for Regression Kaushala Dias and Terry Windeatt Centre for Vision Speech and Signal Processing Faculty of Engineering and Physical Sciences University of

Statistical Machine Learning (CSE 575)

Statistical Machine Learning (CSE 575) About this Course The link between inference and computation is central to statistical machine learning, which combines the computational sciences with statistics.

Sigmoid function is a) Linear B) non linear C) piecewise linear D) combination of linear & non linear

1. Neural networks are also referred to as (multiple answers) A) Neurocomputers B) connectionist networks C) parallel distributed processors D) ANNs 2. The property that permits developing nervous system

ENRICH FRAMEWORK FOR MULTI-DOCUMENT SUMMARIZATION USING TEXT FEATURES AND FUZZY LOGIC

ENRICH FRAMEWORK FOR MULTI-DOCUMENT SUMMARIZATION USING TEXT FEATURES AND FUZZY LOGIC 1 SACHIN PATIL, 2 RAHUL JOSHI 1, 2 Symbiosis Institute of Technology, Department of Computer science, Pune Affiliated

Fuzzy Output Error as the Performance Function for Training Artificial Neural Networks to Predict Reading Comprehension from Eye Gaze

Fuzzy Output Error as the Performance Function for Training Artificial Neural Networks to Predict Reading Comprehension from Eye Gaze Leana Copeland, Tom Gedeon, and Sumudu Mendis Research School of Computer

Intelligent Student Analysis using Fuzzy Logic Shubhanjan Chakrabarty, Shubham Zanwar, Namita Ramakrishna VIT University, Vellore

www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 6 Issue 7 July 2017, Page No. 22138-22144 Index Copernicus value (2015): 58.10 DOI: 10.18535/ijecs/v6i7.39 Abstract:

Discriminative Regularization: A New Classifier. Learning Method

Discriminative Regularization: A New Classifier Learning Method Hui Xue 1 Songcan Chen 1* Qiang Yang 2 1 Department of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics,

CS 229 Project Report Keyword Extraction for Stack Exchange Questions

CS 229 Project Report Keyword Extraction for Stack Exchange Questions Jiaji Hu, Xuening Liu, Li Yi 1 Introduction The Stack Exchange network is a group of questionand-answer websites with each site covering

Development of a Project Selection Method on Information System Using ANP and Fuzzy Logic

Development of a Project Selection Method on Information System Using ANP and Fuzzy Logic Ingu Kim, Shangmun Shin, Yongsun Choi, Nguyen Manh Thang, Edwin R. Ramos, and Won-Joo Hwang Abstract Project selection

Fuzzy Inference System Based on a Model of Affective- Cognitive Criteria for English Learning Achievement

Information Engineering Express International Institute of Applied Informatics 2015, Vol.1, No.3, 39 48 Fuzzy Inference System Based on a Model of Affective- Cognitive Criteria for English Learning Achievement

Neural Network and Neuro Fuzzy Model for Forecasting Equity Market Data

Neural Network and Neuro Fuzzy Model for Forecasting Equity Market Data 1* Berna Seref and 2 Necaattin Barisci *1 Faculty of Engineering, Department of Computer Engineering Dumlupinar University, Turkey

Short Term Load Forecasting of Chhattisgarh Grid Using Adaptive Neuro Fuzzy Inference System

Short Term Load Forecasting of Chhattisgarh Grid Using Adaptive Neuro Fuzzy Inference System Saurabh Ghore 1, Amit Goswami 2 1 M.Tech. Student, Disha Institute of Management and Technology, Raipur, Chhattisgarh,

Automatic Text Summarization

Automatic Text Summarization Trun Kumar Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela-769 008, Odisha, India Automatic text summarization Thesis report

Classification with Deep Belief Networks. HussamHebbo Jae Won Kim

Classification with Deep Belief Networks HussamHebbo Jae Won Kim Table of Contents Introduction... 3 Neural Networks... 3 Perceptron... 3 Backpropagation... 4 Deep Belief Networks (RBM, Sigmoid Belief

Expert System for Heart Problems

Expert System for Heart Problems M. Eswara Rao Asst. Professor, TP Institute of Science & Tech., Komatipalli, Bobbili. haieswar2020@gmail.com Dr. S. Govinda Rao, Scientist (Statistics) ANGR Agrl. University,RARS,

Modelling Student Knowledge as a Latent Variable in Intelligent Tutoring Systems: A Comparison of Multiple Approaches

Modelling Student Knowledge as a Latent Variable in Intelligent Tutoring Systems: A Comparison of Multiple Approaches Qandeel Tariq, Alex Kolchinski, Richard Davis December 6, 206 Introduction This paper

Intelligent tools in business to business training A. Drigas, S. Kouremenos, J. Vrettaros, D. Kouremenos & L. Koukianakis NCSR Demokritos - Department of technological applications Ag. Paraskevi, 15310,

Multi-objective Optimization of Parallel Machine Scheduling Using Neural Networks

Multi-objective Optimization of Parallel Machine Scheduling Using Neural Networks A.Muralidhar Department of Mechanical Engineering Thanthai Periyar Government Institute of Technology, Vellore T. Alwarsamy

Enhancing the Delta Training Rule for a Single Layer Feedforward Heteroassociative Memory Neural Network

Enhancing the Delta Training Rule for a Single Layer Feedforward Heteroassociative Memory Neural Network Omar Waleed Abdulwahhab University of Baghdad College of Engineering Computer Engineering Department

A Neuro-Fuzzy Method to Improving Backfiring Conversion Ratios

A Neuro-Fuzzy Method to Improving Backfiring Conversion Ratios Justin Wong 1, Danny Ho 2, Luiz Fernando Capretz 1 jwong343@uwo.ca, danny@nfa-estimation.com, lcapretz@eng.uwo.ca 1. Department of Electrical

APPLICATION OF FUZZY INFERENCE SYSTEM (FIS) TO CRITERION-REFERENCED ASSESSMENT WITH A CASE STUDY

Proceedings of the 2 nd International Conference of Teaching and Learning (ICTL 2009) INTI University College, Malaysia APPLICATION OF FUZZY INFERENCE SYSTEM (FIS) TO CRITERION-REFERENCED ASSESSMENT WITH

Machine learning theory

Machine learning theory Machine learning theory Introduction Hamid Beigy Sharif university of technology February 27, 2017 Hamid Beigy Sharif university of technology February 27, 2017 1 / 28 Machine learning

Performance Evaluation by Fuzzy Inference Technique

International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-237, Volume-3, Issue-2, May 213 Performance Evaluation by Fuzzy Inference Technique Shruti S Jamsandekar, R.R Mudholkar Abstract:

Evaluation and Comparison of Performance of different Classifiers

Evaluation and Comparison of Performance of different Classifiers Bhavana Kumari 1, Vishal Shrivastava 2 ACE&IT, Jaipur Abstract:- Many companies like insurance, credit card, bank, retail industry require

Applied Multivariate Analysis Prof. Amit Mitra Prof. Sharmishtha Mitra Department of Mathematics and Statistics Indian Institute of Technology, Kanpur

Applied Multivariate Analysis Prof. Amit Mitra Prof. Sharmishtha Mitra Department of Mathematics and Statistics Indian Institute of Technology, Kanpur Prologue Lecture Applied Multivariate Analysis Hello

Introduction. Binary Classification and Bayes Error.

CIS 520: Machine Learning Spring 2018: Lecture 1 Introduction Binary Classification and Bayes Error Lecturer: Shivani Agarwal Disclaimer: These notes are designed to be a supplement to the lecture They

Intelligent Diagnosis of Hepatitis Disease using Union-based Fuzzy Neural Networks

Vol.15 (GCIT 017, pp.3-38 http://dx.doi.org/10.157/astl.017.15.07 Intelligent Diagnosis of Hepatitis Disease using Union-based Fuzzy eural etworks Chang-Wook Han Department of Electrical Engineering, Dong-Eui

Other tasks will ask students to apply understanding of the relationship between similar triangles and slope.

C. Understand the connections between proportional relationships, lines, and linear equations. Tasks for this target will ask students to graph one or more proportional relationships and connect the unit

Lecture 1. Introduction - Part 1. Luigi Freda. ALCOR Lab DIAG University of Rome La Sapienza. October 6, 2016

Lecture 1 Introduction - Part 1 Luigi Freda ALCOR Lab DIAG University of Rome La Sapienza October 6, 2016 Luigi Freda (University of Rome La Sapienza ) Lecture 1 October 6, 2016 1 / 39 Outline 1 General

Survey on Three Fuzzy Inference-based Student Evaluation Methods

Magyar Kutatók 10. Nemzetközi Szimpóziuma 10 th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics Survey on Three Fuzzy Inference-based Student Evaluation Methods

UNIVERSITY OF SURREY

UNIVERSITY OF SURREY B.Sc. Undergraduate Programmes in Computing B.Sc. Undergraduate Programmes in Mathematical Studies Level HE3 Examination MODULE CS364 Artificial Intelligence Time allowed: 2 hours

Available online at ScienceDirect. Procedia Computer Science 61 (2015 ) 18 23

Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 61 (2015 ) 18 23 Complex Adaptive Systems, Publication 5 Cihan H. Dagli, Editor in Chief Conference Organized by Missouri