Feature selection using hybrid Taguchi genetic algorithm and support vector machine

This paper presents a novel approach of hybridizing two conventional machine learning algorithms, Taguchi genetic algorithm (TGA) and support vector machine (SVM), for feature selection. The Taguchi method is an experimental design method, which is inserted between crossover and mutation operations of a GA to enhance the genetic algorithm so that better potential offspring can be generated. The TGA searches for the best feature subset by using principles of evolutionary process, after which the selected feature subset is then passed to the SVM to calculate classification accuracy. Experimental results show that this approach effectively simplifies features selection by reducing the total number of features needed. The proposed method is able to produce good classification accuracy.