Book Review: Computational Methods of Feature Selection

PREFACE Introduction and Background Less Is More Huan Liu and Hiroshi Motoda Background and Basics Supervised, Unsupervised, and Semi-Supervised Feature Selection Key Contributions and Organization of the Book Looking Ahead Unsupervised Feature Selection Jennifer G. Dy Introduction Clustering Feature Selection Feature Selection for Unlabeled Data Local Approaches Summary Randomized Feature Selection David J. Stracuzzi Introduction Types of Randomizations Randomized Complexity Classes Applying Randomization to Feature Selection The Role of Heuristics Examples of Randomized Selection Algorithms Issues in Randomization Summary Causal Feature Selection Isabelle Guyon, Constantin Aliferis, and Andre Elisseeff Introduction Classical "Non-Causal" Feature Selection The Concept of Causality Feature Relevance in Bayesian Networks Causal Discovery Algorithms Examples of Applications Summary, Conclusions, and Open Problems Extending Feature Selection Active Learning of Feature Relevance Emanuele Olivetti, Sriharsha Veeramachaneni, and Paolo Avesani Introduction Active Sampling for Feature Relevance Estimation Derivation of the Sampling Benefit Function Implementation of the Active Sampling Algorithm Experiments Conclusions and Future Work A Study of Feature Extraction Techniques Based on Decision Border Estimate Claudia Diamantini and Domenico Potena Introduction Feature Extraction Based on Decision Boundary Generalities about Labeled Vector Quantizers Feature Extraction Based on Vector Quantizers Experiments Conclusions Ensemble-Based Variable Selection Using Independent Probes Eugene Tuv, Alexander Borisov, and Kari Torkkola Introduction Tree Ensemble Methods in Feature Ranking The Algorithm: Ensemble-Based Ranking against Independent Probes Experiments Discussion Efficient Incremental-Ranked Feature Selection in Massive Data Roberto Ruiz, Jesus S. Aguilar-Ruiz, and Jose C. Riquelme Introduction Related Work Preliminary Concepts Incremental Performance over Ranking Experimental Results Conclusions Weighting and Local Methods Non-Myopic Feature Quality Evaluation with (R)ReliefF Igor Kononenko and Marko Robnik Sikonja Introduction From Impurity to Relief ReliefF for Classification and RReliefF for Regression Extensions Interpretation Implementation Issues Applications Conclusion Weighting Method for Feature Selection in k-Means Joshua Zhexue Huang, Jun Xu, Michael Ng, and Yunming Ye Introduction Feature Weighting in k-Means W-k-Means Clustering Algorithm Feature Selection Subspace Clustering with k-Means Text Clustering Related Work Discussions Local Feature Selection for Classification Carlotta Domeniconi and Dimitrios Gunopulos Introduction The Curse of Dimensionality Adaptive Metric Techniques Large Margin nearest Neighbor Classifiers Experimental Comparisons Conclusions Feature Weighting through Local Learning Yijun Sun Introduction Mathematical Interpretation of Relief Iterative Relief Algorithm Extension to Multiclass Problems Online Learning Computational Complexity Experiments Conclusion Text Classification and Clustering Feature Selection for Text Classification George Forman Introduction Text Feature Generators Feature Filtering for Classification Practical and Scalable Computation A Case Study Conclusion and Future Work A Bayesian Feature Selection Score Based on Naive Bayes Models Susana Eyheramendy and David Madigan Introduction Feature Selection Scores Classification Algorithms Experimental Settings and Results Conclusion Pairwise Constraints-Guided Dimensionality Reduction Wei Tang and Shi Zhong Introduction Pairwise Constraints-Guided Feature Projection Pairwise Constraints-Guided Co-Clustering Experimental Studies Conclusion and Future Work Aggressive Feature Selection by Feature Ranking Masoud Makrehchi and Mohamed S. Kamel Introduction Feature Selection by Feature Ranking Proposed Approach to Reducing Term Redundancy Experimental Results Summary Feature Selection in Bioinformatics Feature Selection for Genomic Data Analysis Lei Yu Introduction Redundancy-Based Feature Selection Empirical Study Summary A Feature Generation Algorithm with Applications to Biological Sequence Classification Rezarta Islamaj Dogan, Lise Getoor, and W. John Wilbur Introduction Splice-Site Prediction Feature Generation Algorithm Experiments and Discussion Conclusions An Ensemble Method for Identifying Robust Features for Biomarker Discovery Diana Chan, Susan M. Bridges, and Shane C. Burgess Introduction Biomarker Discovery from Proteome Profiles Challenges of Biomarker Identification Ensemble Method for Feature Selection Feature Selection Ensemble Results and Discussion Conclusion Model Building and Feature Selection with Genomic Data Hui Zou and Trevor Hastie Introduction Ridge Regression, Lasso, and Bridge Drawbacks of the Lasso The Elastic Net The Elastic-Net Penalized SVM Sparse Eigen-Genes Summary INDEX