An Improved Feature Removal Approach for Classification of High Dimensional Feature Dataset

Abstract

Extracting and evaluating pertinent information in a high-dimensional feature set is extremely difficult when dealing with a high-dimensional feature space. Classification methods require additional training time to generate a classification model. Every feature is not equally relevant in a high-dimensional feature collection. As a result, feature selection is a productive method for identifying vital features and eliminating unnecessary ones. Feature selection serves as a method of pre-processing before classification. It decreases the dimension of the dataset to shorten the training period required to develop a classifier. This research study aims to propose a novel feature subset selection method that establishes the relative importance of each feature using several criteria. The proposed approach ranks available features from high to low using a variety of feature-ranking approaches. Different feature ranking algorithms perform differently on the same dataset. It is challenging to obtain robust performance with just one feature ranking algorithm. To overcome this problem, we have used the Schulze rank aggregation method. The Schulze method combines multiple feature ranking techniques to assign a rank to each feature inside the dataset. This study presents an optimization strategy for heuristic search based on the backward feature removal method. It eliminates features according to the rank determined by the Schulze rank aggregation technique. In this paper, we evaluated the performance of the proposed method against the current state-of-the-art feature ranking techniques for high-dimensional feature set classification.