Multi Filtration Feature Selection (MFFS) to improve discriminatory ability in clinical data set

Abstract

Selection of optimal features is an important area of research in medical data mining systems. In this paper we introduce an efficient four-stage procedure – feature extraction, feature subset selection, feature ranking and classification, called as Multi-Filtration Feature Selection (MFFS), for an investigation on the improvement of detection accuracy and optimal feature subset selection. The proposed method adjusts a parameter named “variance coverage” and builds the model with the value at which maximum classification accuracy is obtained. This facilitates the selection of a compact set of superior features, remarkably at a very low cost. An extensive experimental comparison of the proposed method and other methods using four different classifiers (Naïve Bayes (NB), Support Vector Machine (SVM), multi layer perceptron (MLP) and J48 decision tree) and 22 different medical data sets confirm that the proposed MFFS strategy yields promising results on feature selection and classification accuracy for medical data mining field of research.