A Novel Subset Feature Selection Framework for Increasing the Classification Performance of SONAR Targets

Abstract

Proposing a strong subset of feature for a classifier to detect the SONAR (sound navigation and ranging) target is the most valuable subject in the sonar data analysis for the safety of naval vessels. In this research article, we introduced a novel generalized feature selection framework to determine the best subset of features for classifying sonar data set based on Symmetrical Uncertainty(SU). Using the proposed framework, we tried to form ‘M’ candidate subsets of features. Each set comprises of a finite number of features without any ingeminate. The resultant subset is analyzed using various Tree, Lazy, Bayesian, and Rule based classifiers. As each subset formed by proposed method consists of limited number of features in it, an equal number of top features extracted by existing filter based feature selection methods( Chi Squared -Chi, Information Gain(IG), Gain Ratio(GR), and ReliefF(Rel)) are taken into consideration, and analyzed with the same classifiers. After careful investigation of obtained results by existing and proposed methods, it has been noticed that minimum one of the candidate subset of features outperforming than some of the existing methods. For this experiment, a real time SONAR dataset found at UCI machine learning repository is considered. This framework also applied on other benchmark datasets to make it generalized.