An Efficient Method for Variables Selection Using SVM-Based Criteria
نویسندگان
چکیده
The problem of feature selection for Support Vector Machines (SVMs) classification is investigated in the linear two classes case. We suggest a new method of feature selection based on ranking scores derived from SVMs. We analyze the retraining effects on the ranking rules based on these scores. Our features selection algorithm consists in a forward selection strategy according to the decreasing order of the variables importance and it allows to simply determine how many selected features must be provided to the predictor. Finally we illustrate the effectiveness of our approach on linear synthetic data and some challenging benchmark problems based on Microarray data. Results demonstrate a significant improvement of generalization performance using a few variables. keyword Support vector machines (SVMs), Feature selection, SVM-based criteria, Ranking rules, Bounds and margin sensitivity, Forward selection, Subset search strategy, Bootstrap, Cross validation, Microarray data.
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