The Significant Matrix without Genetic Algorithm for the feature selection (Significant Matrix 2)

Abstract

This paper presents to the improvement of the Significant Matrix [1] that works along with Genetic Algorithm in feature selection of appropriate data for a decision tree structure. This work proposes the reduction of time that cut off the Genetic Algorithm's work times. The new method is proposed in the name “Significant Matrix 2” which is calculated from the relationship between categorical data and a class label for determining the threshold of the feature selection and the sub-dataset from the method contains the appropriate feature to create decision trees. The results of experiment of feature selection times. The proposed work can work faster than [1], average 28 times and the results of experiments of the decision tree model is constructed from the feature of the method and model of neural networks. The proposed work gives the average accuracy of the classification at 95.9% of the 11 sample database, also a number of the data features are less than a number of the features from the method of neural networks [6] that uses the feature only 48.08% from all feature in example dataset. Furthermore, when comparing the accuracy of the classification decision tree which another feature selected method. This proposed work have the amount of average accuracy higher than the selected data from another method. Experimental results show that the proposed method does not only provide a higher accuracy, but reduce the complexity by using less features of the dataset.