Contents......Page 7 An Introduction to Feature Extraction......Page 15 Part I Feature Extraction Fundamentals......Page 41 1 Learning Machines......Page 43 2 Assessment Methods......Page 79 3 Filter methods......Page 103 4 Search Strategies......Page 133 5 Embedded Methods......Page 153 6 Information-Theoretic Methods......Page 183 7 Ensemble Learning......Page 203 8 Fuzzy Neural Networks......Page 223 Part II Feature Selection Challenge......Page 253 9 Design and Analysis of the NIPS2003 Challenge......Page 255 10 High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees......Page 283 11 Ensembles of Regularized Least Squares Classifiers for High-Dimensional Problems......Page 315 12 Combining SVMs with Various Feature Selection Strategies......Page 333 13 Feature Selection with Transductive Support Vector Machines......Page 343 14 Variable Selection using Correlation and Single Variable Classifier Methods: Applications......Page 361 15 Tree-Based Ensembles with Dynamic Soft Feature Selection......Page 377 16 Sparse, Flexible and Efficient Modeling using L1 Regularization......Page 393 17 Margin Based Feature Selection and Infogain with Standard Classifiers......Page 413 18 Bayesian Support Vector Machines for Feature Ranking and Selection......Page 421 19 Nonlinear Feature Selection with the Potential Support Vector Machine......Page 437 20 Combining a Filter Method with SVMs......Page 455 21 Feature Selection via Sensitivity Analysis with Direct Kernel PLS......Page 463 22 Information Gain, Correlation and Support Vector Machines......Page 479 23 Mining for Complex Models Comprising Feature Selection and Classification......Page 487 24 Combining Information-Based Supervised and Unsupervised Feature Selection......Page 505 25 An Enhanced Selective Naïve Bayes Method with Optimal Discretization......Page 515 26 An Input Variable Importance Definition based on Empirical Data Probability Distribution......Page 523 Part III New Perspectives in Feature Extraction......Page 531 27 Spectral Dimensionality Reduction......Page 533 28 Constructing Orthogonal Latent Features for Arbitrary Loss......Page 561 29 Large Margin Principles for Feature Selection......Page 591 30 Feature Extraction for Classification of Proteomic Mass Spectra: A Comparative Study......Page 611 31 Sequence motifs: highly predictive features of protein function......Page 631 Appendix A Elementary Statistics......Page 653 Elementary Statistics......Page 655 References......Page 669 Appendix B Feature Selection Challenge Datasets......Page 671 Experimental design......Page 673 Arcene......Page 677 Gisette......Page 685 Dexter......Page 689 Dorothea......Page 693 Madelon......Page 697 Matlab code of the lambda method......Page 703 Matlab code used to generate Madelon......Page 705 Appendix C Feature Selection Challenge Fact Sheets......Page 711 10 High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees......Page 713 11 Ensembles of Regularized Least Squares Classifiers for High-Dimensional Problems......Page 715 12 Combining SVMs with Various Feature Selection Strategies......Page 717 13 Feature Selection with Transductive Support Vector Machines......Page 719 14 Variable Selection using Correlation and SVC Methods: Applications......Page 721 15 Tree-Based Ensembles with Dynamic Soft Feature Selection......Page 723 16 Sparse, Flexible and Efficient Modeling using L1 Regularization......Page 725 17 Margin Based Feature Selection and Infogain with Standard Classifiers......Page 727 18 Bayesian Support Vector Machines for Feature Ranking and Selection......Page 729 19 Nonlinear Feature Selection with the Potential Support Vector Machine......Page 731 20 Combining a Filter Method with SVMs......Page 733 21 Feature Selection via Sensitivity Analysis with Direct Kernel PLS......Page 735 22 Information Gain, Correlation and Support Vector Machines......Page 737 23 Mining for Complex Models Comprising Feature Selection and Classification......Page 739 24 Combining Information-Based Supervised and Unsupervised Feature Selection......Page 741 25 An Enhanced Selective Naïve Bayes Method with Optimal Discretization......Page 743 26 An Input Variable Importance Definition based on Empirical Data Probability Distribution......Page 745 Appendix D Feature Selection Challenge Results Tables......Page 747 Result Tables of the NIPS2003 Challenge......Page 749 Arcene......Page 751 Dexter......Page 755 Dorothea......Page 759 Gisette......Page 763 Madelon......Page 767 Overall results......Page 771 Index......Page 775
