Modified-MCA Based Feature Selection Model for Classification

Abstract

A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. A good feature set that contains highly correlated features with the class not only improves the efficiency of the classification algorithms but also improve the classification accuracy. Modified-Multiple Correspondence Analysis (M-MCA or MCA with Geometrical Representation) explores the correlation between different features and classes to score the features for feature selection. The dependence between a feature and a class is measured by a derived value from χ 2 distance called the p-value. It is a standard measure of the reliability of a relation and is examined by p-value. The smaller the p-value, the higher the possibility of the correlation between a feature and a class is true. In this paper, the conventional confidence interval of Multiple Correspondence Analysis (MCA) is modified to get smaller p-value and be more reliable. To evaluate the performance of proposed Modified-MCA, experiments are carried out on benchmark datasets identified and provided by WEKA and UCI repository. In the experiments, Naïve Bayes, Decision Table and JRip are used as the classifiers. The proposed Modified-MCA demonstrates promising results and performs better than well-known feature selection, MCA. The results show that the proposed method outperforms in terms of classification accuracy and reduces the size of feature subspace significantly.