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In an era where accumulating data is easy and storing it inexpensive, featureselection plays a central role in helping to reduce the high-dimensionality ofhuge amounts of otherwise meaningless data. In this paper, we propose agraph-based method for feature selection that ranks features by identifying themost important ones into arbitrary set of cues. Mapping the problem on anaffinity graph-where features are the nodes-the solution is given by assessingthe importance of nodes through some indicators of centrality, in particular,the Eigen-vector Centrality (EC). The gist of EC is to estimate the importanceof a feature as a function of the importance of its neighbors. Ranking centralnodes individuates candidate features, which turn out to be effective from aclassification 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 comparedagainst filter, embedded and wrappers methods. The results are remarkable interms of accuracy, stability and low execution time.

Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality