The feature selection for classification by applying the Significant Matrix with SPEA2

This paper presents a novel application of Genetic Algorithm for the feature selection. The main purpose is to provide proper subset features for decision tree construction in the classification task. New method with the use of “Significant Matrix” on genetic algorithm is presented. The main function is to calculate the relationship between the feature and class label assigned to a fitness value for the population. The algorithm presented important features selected by considering the class of the data and number of features for the least amount in the Significant Matrix. The next step will then update the feature number and the record number to repeat the process until a stop condition is met. Classification by decision tree is used to verify the importance of the features selected by the proposed method. The model tested with 11 different datasets. The results show that the method yields high accuracy of the classification and higher satisfaction compared to classification using artificial neural network. Experimental results show that the proposed method not only provides a higher accuracy, but also reduce the complexity by using less features of the dataset.