Feature selection and ensemble methods for bioinformatics : algorithmic classification and implementations

1. Biological background -- 2. Gene expression data sets -- 3. Introduction to data classification -- 4. Nai͏̈ve Bayes -- 5. Nearest neighbor -- 6. Classification tree -- 7. Support vector machines -- 8. Introduction to feature and gene selection -- 9. Feature selection based on elements of game theory -- 10. Kernel-based feature selection with the hilbert-schmidt independence criterion -- 11. Extreme value distribution based gene selection -- 12. Evolutionary algorithm for identifying predictive genes -- 13. Redundancy-based feature selection -- 14. Unsupervised feature selection -- 15. Differential evolution for finding predictive gene subsets -- 16. Ensembles of classifiers / Oleg Okun -- 17. Classifier ensembles built on subsets of features -- 18. Bagging and random forests -- 19. Boosting and AdaBoost -- 20. Ensemble gene selection -- 22. ROC curve, area under it, other classification performance characteristics and statistical tests -- 23. Bolstered resubstitution error -- 24. Performance evaluation: final check -- 25. Application examples -- 26. End remarks