A Review on Machine Learning Algorithms, Tasks and Applications

Transcription

1 A Review on Machine Learning Algorithms, Tasks and Applications Diksha Sharma 1, Neeraj Kumar 2 ABSTRACT: Machine learning is a field of computer science which gives computers an ability to learn without being explicitly programmed. Machine learning is used in a variety of computational tasks where designing and programming explicit algorithms with good performance is not easy. Applications include filtering, recognition of network intruders or malicious insiders working towards a data breach. One of the foundation objectives of machine learning is to train computers to utilize data to solve a specified problem. A good number of applications of machine learning like classifier training on messages in order to differentiate between spam and non-spam messages, fraud detection etc. In this article we will focus on basics of machine learning, machine learning tasks and problems and various machine learning algorithms. Keywords: Machine learning, supervised learning, unsupervised learning, classification 1. INTRODUCTION Machine learning is a branch of artificial intelligence that allows computer systems to learn directly from examples, data, and experience. Through enabling computers to perform specific tasks intelligently, machine learning systems can carry out complex processes by learning from data, rather than following pre-programmed rules. Increasing data accessibility has endorsed machine learning systems to be trained on a bulky pool of examples, while growing computer processing power has supported the critical capabilities of these systems. Within the field itself there have also been algorithmic advances, which have given machine learning better power. As a outcome of these advances, systems which performed at 1548

2 noticeably below-human levels can now go better than humans at some definite tasks. Many people now cooperate with systems based on machine learning each day, for example in image recognition systems. Now-a-days the concept of machine learning is used in many applications and is a core concept for intelligent systems [1][3].As the field develops further, machine learning shows promise of supporting potentially transformative advances in a range of areas, and the social and economic opportunities which follow are significant. In healthcare, machine learning is creating systems that can assist doctors give more correct or efficient diagnosis for definite conditions. For public services it has the potential to target support more effectively to those in need, or to tailor services to users. Machine learning is helping to make sense of the gigantic quantity of data accessible to researchers today, offering new insights into biology, physics & medicine. II. MACHINE LEARNING TASKS Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. Supervised learning Unsupervised learning Reinforcement learning Supervised Learning: It is the machine learning task of inferring a function from labeled training data. The training data consists of a set of training examples. A supervised learning algorithm analyzes the training data and produces an inferred function that can be utilized for mapping fresh examples. To work out on a given problem of supervised learning, one has to carry out the following steps: (i) Decide the kind of training examples. The user should decide what kind of data is to be used as a training set. (ii) Collect a training set. The training set needs to be envoy of the real-world use of the function. Thus, a set of input objects is collected and corresponding outputs are also collected. (iii) Decide the input feature depiction of the learned function. The accuracy of the learned function relies sturdily on how the input object is represented. Normally, the input object is altered into a feature vector that contains a number of features that are descriptive of the object. The number of features should not be too large. 1549

3 (iv) Decide the structure of the learned function and corresponding learning algorithm. (v) Complete the design. Run the learning algorithm on the gathered training set. Some supervised learning algorithms need the user to find out certain control parameters. (vi) Assess the accuracy of the learned function. After parameter adjustment and learning, the performance of the resulting function should be measured on a test set that is separate from the training set. Unsupervised learning: It is the machine learning task of inferring a function to depict concealed structure from "unlabeled" data. Since the examples specified to the learner are unlabeled, there is no assessment of the accuracy of the structure that is output by the relevant algorithm which is one way of distinguishing unsupervised learning from supervised learning and reinforcement learning. A central case of unsupervised learning is the problem of density estimation in statistics [1]. Reinforcement learning: A computer program interacts with a vibrant environment in which it must perform a certain goal. The program is provided feedback in terms of rewards and punishments as it navigates its problem space. III. MACHINE LEARNING ALGORITHMS There are number of machine learning algorithms such as Linear Regression, Logistic Regression, Decision Tree, SVM [2], and KNN. Linear Regression is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). Here, we establish relationship between independent and dependent variables by fitting a best line. Logistic Regression is used to estimate discrete values based on given set of independent variable(s). In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function. Decision Tree is a type of supervised learning algorithm that is mostly used for classification problems.svm is a classification method. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. K nearest neighbors is a simple algorithm which stores the entire available cases and classifies new cases by a majority vote of its k neighbors. 1550

4 V.CONCLUSION The article illustrates the concept of machine learning with its tasks and applications. The article also highlights the various types of learning such as supervised learning, unsupervised learning and reinforcement learning. In this article a detailed procedure for solving a problem using supervised learning has also been discussed.. VI. REFERENCES 1. Talwar, A. and Kumar, Y., Machine Learning: An artificial intelligence methodology. International Journal of Engineering and Computer Science, 2, pp Fig.1: Machine learning algorithms IV. MACHINE LEARNING APPLICATIONS Machine learning algorithms are widely used in variety of applications like digital image processing(image recognition)[5], big data analysis[4], Speech Recognition, Medical Diagnosis, Statistical Arbitrage, Learning Associations, Classification, Prediction etc. 2. Muhammad, I. and Yan, Z., Supervised Machine Learning Approaches: A Survey. ICTACT Journal on Soft Computing, 5(3). 3. Singh, S., Kumar, N. and Kaur, N., Design Anddevelopment Of Rfid Based Intelligent Security System. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume, Sharma, D., Pabby, G. and Kumar, N., Challenges Involved in Big Data Processing & Methods to Solve Big Data Processing Problems.IJRASET,5(8),pp Kumar, N. and Gupta, S., Offline Handwritten Gurmukhi Character Recognition: A Review. International Journal of Software Engineering and Its Applications, 10(5), pp

5 Ms. Diksha completed her B.Tech from Chitkara University, Himachal Pradesh in the stream of Electronics and Communication Engineering. She is now planning to pursue Masters in science from abroad. Mr. Neeraj Kumar is presently working as Assistant Professor in Electronics and Communication Engineering Department at Chitkara University, Himachal Pradesh, India. He has more than 6 years of teaching experience. His area of interest is digital image processing, digital signal processing. 1552