Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality

TL;DR

This paper introduces a graph-based feature selection method using eigenvector centrality to identify important features, demonstrating improved accuracy, stability, and efficiency across diverse datasets.

Contribution

It proposes a novel feature ranking approach based on eigenvector centrality in a feature affinity graph, enhancing feature selection effectiveness.

Findings

Improved classification accuracy on multiple datasets

Demonstrated stability and low computational cost

Outperformed traditional filter, embedded, and wrapper methods

Abstract

In an era where accumulating data is easy and storing it inexpensive, feature selection plays a central role in helping to reduce the high-dimensionality of huge amounts of otherwise meaningless data. In this paper, we propose a graph-based method for feature selection that ranks features by identifying the most important ones into arbitrary set of cues. Mapping the problem on an affinity graph-where features are the nodes-the solution is given by assessing the importance of nodes through some indicators of centrality, in particular, the Eigen-vector Centrality (EC). The gist of EC is to estimate the importance of a feature as a function of the importance of its neighbors. Ranking central nodes individuates candidate features, which turn out to be effective from a classification point of view, as proved by a thoroughly experimental section. Our approach has been tested on 7 diverse datasets from recent literature (e.g., biological data and object recognition, among others), and compared against filter, embedded and wrappers methods. The results are remarkable in terms of accuracy, stability and low execution time.