COMPUTATIONAL BUSINESS ANALYTICS

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1 COMPUTATIONAL BUSINESS ANALYTICS SUBRATA DAS Machine Analytics, Inc. Belmont, Massachusetts, USA Co* CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Croup, an informa business A CHAPMAN & HALL BOOK

2 Contents CHAPTER 1 Analytics Background and Architectures ANALYTICS DEFINED ANALYTICS MODELING ANALYTICS PROCESSES Information Hierarchy Information Processing Hierarchy Human Information Processing Hierarchy ANALYTICS AND DATA FUSION JDL Fusion Model OODA Loop FURTHER READING 15 Chapter 2 Mathematical and Statistical Preliminaries STATISTICS AND PROBABILITY THEORY LINEAR ALGEBRA FUNDAMENTALS MATHEMATICAL LOGIC GRAPHS AND TREES MEASURES OF PERFORMANCE ALGORITHMIC COMPLEXITY FURTHER READING 41 Chapter 3 Statistics for Descriptive Analytics PROBABILITY DISTRIBUTIONS DISCRETE PROBABILITY DISTRIBUTIONS Binomial and Multinomial Distributions Poisson Distribution and Process 48

3 vi Contents 3.3 CONTINUOUS PROBABILITY DISTRIBUTIONS Gaussian or Normal Distribution Lognormal Exponential Distribution Weibull Distribution Beta and Dirichlet Distributions Gamma Distribution GOODNESS-OF-FIT TEST Probability Plot One-Way Chi-Square Goodness-of-Fit Test Kolmogorov-Smirnov Test FURTHER READING 64 Chapter 4 Bayesian Probability and Inference BAYESIAN INFERENCE PRIOR PROBABILITIES Conjugate Priors The Jeffreys Prior FURTHER READING 73 Chapter 5 Inferential Statistics and Predictive Analytics CHI-SQUARE TEST OF INDEPENDENCE REGRESSION ANALYSES Simple Linear Regression Multiple Linear Regression Logistic Regression Polynomial Regression BAYESIAN LINEAR REGRESSION Gaussian Processes PRINCIPAL COMPONENT AND FACTOR ANALY SES SURVIVAL ANALYSIS AUTOREGRESSION MODELS FURTHER READING 98

4 Contents vii Chapter 6 Artificial Intelligence for Symbolic Analytics ANALYTICS AND UNCERTAINTIES Ignorance to Uncertainties Approaches to Handling Uncertainties NEO-LOGICIST APPROACH Evolution of Rules Inferencing in Rule-based Systems Advantages and Disadvantages of Rule-Based Systems NEO-PROBABILIST NEO-CALCULIST APPROACH Certainty Factors Dempster-Shafer Theory of Belief Function NEO- GRANULARIS T Probabilistic Logic Fuzzy Logic Fuzzy Logic for Customer Segmentation FURTHER READING 134 Chapter 7 Probabilistic Graphical Modeling NAIVE BAYESIAN CLASSIFIER (NBC) K-DEPENDENCE NAIVE BAYESIAN CLASSIFIER (KNBC) BAYESIAN BELIEF NETWORKS Conditional Independence in Belief Networks Evidence, Belief, and Likelihood Prior Probabilities in Networks without Evidence Belief Revision Evidence Propagation in Polytrees Upward Propagation in a Linear Frag ment Downward Propagation in a Linear Fragment Upward Propagation in a Tree Fragment 167

5 viii Contents Downward Propagation in a Tree Frag ment Upward Propagation in a Polytree Frag ment Downward Propagation in a Polytree Fragment Propagation Algorithm Evidence Propagation in Directed Acyclic Graphs Graphical Transformation Join Tree Initialization Propagation in Join Tree and Marginalization Handling Evidence Complexity of Inference Algorithms Acquisition of Probabilities Advantages and Disadvantages of Belief Networks Belief Network Tools FURTHER READING 199 Chapter 8 Decision Support and Prescriptive Analytics EXPECTED UTILITY THEORY AND DECISION TREES INFLUENCE DIAGRAMS FOR DECISION SUPPORT Inferencing in Influence Diagrams Compilation of Influence Diagrams SYMBOLIC ARGUMENTATION FOR DECISION SUP PORT Measuring Consensus Combining Sources of Varying Confidence FURTHER READING 226 CHAPTER 9 Time Series Modeling and Forecasting PROBLEM MODELING State Transition and Observation Models Estimation Problem KALMAN FILTER (KF) 233

6 Contents ix Extended Kalman Filter (EKF) MARKOV MODELS Hidden Markov Models (HMM) The Forward Algorithm The Viterbi Algorithm Baum-Welch Algorithm for Learning HMM DYNAMIC BAYESIAN NETWORKS (DBNS) Inference Algorithms for DBNs FURTHER READING 265 CHAPTER 10 Monte Carlo Simulation MONTE CARLO APPROXIMATION GIBBS SAMPLING METROPOLIS-HASTINGS ALGORITHM PARTICLE FILTER (PF) Particle Filter for Dynamical Systems Particle Filter for DBN Particle Filter Issues FURTHER READING 280 Chapter 11 Cluster Analysis and Segmentation HIERARCHICAL CLUSTERING K-MEANS CLUSTERING K-NEAREST NEIGHBORS SUPPORT VECTOR MACHINES Linearly Separable Data Preparation of Data and Packages Non-Separable Data Non-Linear Classifier VC Dimension and Maximum Margin Classifier NEURAL NETWORKS Model Building and Data Preparation Gradient Descent for Updating Weights FURTHER READING 302

7 x Contents Chapter 12 Machine Learning for Analytics Models DECISION TREES Algorithms for Constructing Decision Trees Overfltting in Decision Trees Handling Continuous Attributes Advantages and Disadvantages of Decision Tree Techniques LEARNING NAIVE BAYESIAN CLASSIFIERS Semi-Supervised Learning of NBC via EM LEARNING OF KNBC LEARNING OF BAYESIAN BELIEF NETWORKS Cases for Learning Bayesian Networks Learning Probabilities Brief Survey Learning Probabilities from Fully Ob servable Variables Learning Probabilities from Partially Observable Variables Online Adjustment of Parameters Structure Learning Brief Survey Learning Structure from Fully Observ able Variables Learning Structure from Partially Ob servable Variables Use of Prior Knowledge from Experts INDUCTIVE LOGIC PROGRAMMING FURTHER READING 343 Chapter 13 Unstructured Data and Text Analytics INFORMATION STRUCTURING AND EXTRAC TION BRIEF INTRODUCTION TO NLP Syntactic Analysis Tokenization Morphological Analysis 349

8 Contents xi Part-of-Speech (POS) Tagging Syntactic Parsing Semantic Analysis Named Entity Recognition Co-reference Resolution Relation Extraction TEXT CLASSIFICATION AND TOPIC EXTRAC TION Naive Bayesian Classifiers (NBC) k-dependence Naive Bayesian Classifier (knbc) Latent Semantic Analysis Probabilistic Latent Semantic Analysis (PLSA) Latent Dirichlet Allocation (LDA) FURTHER READING 372 Chapter 14 Semantic Web RESOURCE DESCRIPTION FRAMEWORK (RDF) RDF Schema (RDFS) Ontology Web Language (OWL) DESCRIPTION LOGICS Description Logic Syntax Description Logic Axioms Description Logic Constructs and Subsystems Description Logic and OWL Constructs in Re lational Database Description Logic as First-Order Logic FURTHER READING 388 Chapter 15 Analytics Tools INTELLIGENT DECISION AIDING SYSTEM (IDAS) ENVIRONMENT FOR 5TH GENERATION APPLI CATIONS (E5) Rule-based Expert System Shell Prolog Interpreter Lisp Interpreter 405

9 xii Contents 15.3 ANALYSIS OF TEXT (ATEXT) R AND MATLAB SAS AND WEKA 421 Chapter 16 Analytics Case Studies RISK ASSESSMENT MODEL RISK ASSESSMENT IN INDIVIDUAL LENDING US ING IDAS RISK ASSESSMENT IN COMMERCIAL LENDING USING E5 AND IDAS FRAUD DETECTION SENTIMENT ANALYSIS USING ATEXT Text Corpus Classification Evaluation Results LIFE STATUS ESTIMATION USING DYNAMIC BAYESIAN NETWORKS 449 Appendix A Usage of Symbols 453 A.l SYMBOLS USED IN THE BOOK 453 Appendix B Examples and Sample Data 455 B.l PLAY-TENNIS EXAMPLE 455 B.2 UNITED STATES ELECTORAL COLLEGE DATA 456 Appendix C MATLAB and R Code Examples 457 C.l MATLAB CODE FOR STOCK PREDICTION USING KALMAN FILTER 457 C.2 R CODE FOR STOCK PREDICTION USING KALMAN FILTER 460 Index 479

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