Topological Feature Selection

TL;DR

This paper presents a new unsupervised, graph-based feature selection method that uses topological network structures to identify relevant features, offering high adaptability, explainability, and computational efficiency.

Contribution

It introduces a novel topologically constrained network approach for feature selection that is tunable, explainable, and computationally cheaper than existing methods.

Findings

Outperforms or matches state-of-the-art on 16 benchmark datasets

Highly adaptable to different data types

Computationally efficient compared to alternatives

Abstract

In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations. We model dependency structures among features using a family of chordal graphs (the Triangulated Maximally Filtered Graph), and we maximise the likelihood of features' relevance by studying their relative position inside the network. Such an approach presents three aspects that are particularly satisfactory compared to its alternatives: (i) it is highly tunable and easily adaptable to the nature of input data; (ii) it is fully explainable, maintaining, at the same time, a remarkable level of simplicity; (iii) it is computationally cheaper compared to its alternatives. We test our algorithm on 16 benchmark datasets from different applicative domains showing that it outperforms or matches the current state-of-the-art under heterogeneous evaluation conditions.