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A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithms
In this paper a hybrid feature selection method is proposed which takes advantages of wrapper subset evaluation with a lower cost and improves the performance of a group of classifiers.
Improving Performance of a Group of Classification Algorithms Using Resampling and Feature Selection
Finally, we apply some well- known classification algorithms (Na\"ive Bayes, Logistic, Multilayer Perceptron, Best First Decision Tree and JRIP) to the resulting dataset and compare the results and prediction rates before and after the application of our feature selection method on that dataset.
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