A Hybrid Graph Centrality Based Feature Selection Approach for Supervised Learning

Abstract

Graph-based approaches of feature selection have found their application in diverse areas owing to their efficacy to detect potential connections among the features. In this paper, we propose a hybrid graph-based approach for selecting a subset of features in the context of supervised learning. The novelty of this approach lies in the utilisation of graph-theoretic concepts of degree centrality as well as eigenvector centrality to assess the relative importance of features. Additionally, entropy-based measures have been used. The performance of the proposed approach is compared to that of the existing benchmark algorithms using publicly available datasets. The results have come out to be quite encouraging.