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In this paper a hybrid feature selection method is proposed which takesadvantages of wrapper subset evaluation with a lower cost and improves theperformance of a group of classifiers. The method uses combination of sampledomain filtering and resampling to refine the sample domain and two featuresubset evaluation methods to select reliable features. This method utilizesboth feature space and sample domain in two phases. The first phase filters andresamples the sample domain and the second phase adopts a hybrid procedure byinformation gain, wrapper subset evaluation and genetic search to find theoptimal feature space. Experiments carried out on different types of datasetsfrom UCI Repository of Machine Learning databases and the results show a risein the average performance of five classifiers (Naive Bayes, Logistic,Multilayer Perceptron, Best First Decision Tree and JRIP) simultaneously andthe classification error for these classifiers decreases considerably. Theexperiments also show that this method outperforms other feature selectionmethods with a lower cost.

A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms