Feature Selection Using Genetic Algorithm with Mutual Information

Feature selection is the problem of selecting a subset of features without reducing the accuracy of representing the original set of features. It is the most important step that affects the performance of a pattern recognition system. In this paper, genetic algorithm (GA) is used to implement a feature selection in filter based method, and the mutual information is served as a fitness function of GA and k-NN is used to evaluate the accuracy of the selected feature. The proposed feature selection method is applied to the features extracted from the Lung CT scan images. Experimental results shows that proposed feature selection method simplifies features effectively and obtains a higher classification accuracy compared to the unreduced dataset classification accuracy.