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Contents xv

8.5 Model Evaluation and Selection 364
8.5.1 Metrics for Evaluating Classifier Performance 364
8.5.2 Holdout Method and Random Subsampling 370
8.5.3 Cross-Validation 370
8.5.4 Bootstrap 371
8.5.5 Model Selection Using Statistical Tests of Significance 372
8.5.6 Comparing Classifiers Based on Cost–Benefit and ROC Curves 373
8.6 Techniques to Improve Classification Accuracy 377
8.6.1 Introducing Ensemble Methods 378
8.6.2 Bagging 379
8.6.3 Boosting and AdaBoost 380
8.6.4 Random Forests 382
8.6.5 Improving Classification Accuracy of Class-Imbalanced Data 383
8.7 Summary 385
8.8 Exercises 386
8.9 Bibliographic Notes 389
Chapter 9 Classification: Advanced Methods 393
9.1 Bayesian Belief Networks 393
9.1.1 Concepts and Mechanisms 394
9.1.2 Training Bayesian Belief Networks 396
9.2 Classification by Backpropagation 398
9.2.1 A Multilayer Feed-Forward Neural Network 398
9.2.2 Defining a Network Topology 400
9.2.3 Backpropagation 400
9.2.4 Inside the Black Box: Backpropagation and Interpretability 406
9.3 Support Vector Machines 408
9.3.1 The Case When the Data Are Linearly Separable 408
9.3.2 The Case When the Data Are Linearly Inseparable 413
9.4 Classification Using Frequent Patterns 415
9.4.1 Associative Classification 416
9.4.2 Discriminative Frequent Pattern–Based Classification 419
9.5 Lazy Learners (or Learning from Your Neighbors) 422
9.5.1 k-Nearest-Neighbor Classifiers 423
9.5.2 Case-Based Reasoning 425
9.6 Other Classification Methods 426
9.6.1 Genetic Algorithms 426
9.6.2 Rough Set Approach 427
9.6.3 Fuzzy Set Approaches 428
9.7 Additional Topics Regarding Classification 429
9.7.1 Multiclass Classification 430