Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An introduction to variable and feature selection
The Journal of Machine Learning Research
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Monte Carlo feature selection for supervised classification
Bioinformatics
Relationship preserving feature selection for unlabelled clinical trials time-series
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Random forest-based prediction of protein sumoylation sites from sequence features
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
An efficient statistical feature selection approach for classification of gene expression data
Journal of Biomedical Informatics
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Selecting an appropriate classifier for a particular biological application poses a difficult problem for researchers and practitioners alike. We propose a novel measure for assessing the suitability of machine classifiers for particular problems called "win percentage." We define win percentage as the probability a classifier will perform better than its peers on a finite random sample of feature sets, giving each classifier equal opportunity to find suitable features. We illustrate the utility of this method using synthetic data. Then, we evaluate six classifiers in analyzing eight microarray datasets representing three diseases: breast cancer, multiple myeloma, and neuroblastoma. Fundamentally, we illustrate that the selection of the most suitable classifier (i.e., one that is more likely to perform better than its peers) not only depends on the dataset and application but also on the thoroughness of feature selection. In particular, win percentage provides a single measurement that could assist users in eliminating or selecting classifiers for their particular application and will be accessible from www.biomiblab.org.