A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Learning with many irrelevant features
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Feature selection in MLPs and SVMs based on maximum output information
IEEE Transactions on Neural Networks
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The data collected from a typical microarray experiment usually consists of tens of samples and thousands of genes (i.e., features). Usually only a small subset of features is relevant and non-redundant to differentiate the samples. Identifying an optimal subset of relevant genes is crucial for accurate classification of samples. In this paper, we propose a method for relevant gene subset selection for microarray gene expression data. Our method is based on gap tolerant classifier, a variation of support vector machine, and uses a hill-climbing search strategy. Unlike most other hill-climbing approaches, where classification accuracies are used as a criterion for feature selection, the proposed method uses a mixture of accuracy and SVM margin to select features. Our experimental results show that this strategy is effective both in selecting relevant and in eliminating redundant features.