Infinite Feature Selection: A Graph-based Feature Filtering Approach

TL;DR

This paper introduces Infinite Feature Selection (Inf-FS), a graph-based filtering method that evaluates feature subsets as paths, allowing for an elegant ranking of features by considering infinite-length paths, and demonstrates superior performance across multiple benchmarks.

Contribution

The paper presents a novel graph-based feature selection framework that evaluates infinite-length feature paths, providing an efficient and effective way to rank features and select relevant subsets.

Findings

Inf-FS outperforms 18 comparative methods on 11 benchmarks.

The framework effectively handles diverse feature types and settings.

It provides a flexible, unsupervised feature subset selection strategy.

Abstract

We propose a filtering feature selection framework that considers subsets of features as paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) relations among features, dealing with relevance and redundancy principles. By two different interpretations (exploiting properties of power series of matrices and relying on Markov chains fundamentals) we can evaluate the values of paths (i.e., feature subsets) of arbitrary lengths, eventually go to infinite, from which we dub our framework Infinite Feature Selection (Inf-FS). Going to infinite allows to constrain the computational complexity of the selection process, and to rank the features in an elegant way, that is, considering the value of any path (subset) containing a particular feature. We also propose a simple unsupervised strategy to cut the ranking, so providing the subset of features to keep. In the experiments, we analyze diverse settings with heterogeneous features, for a total of 11 benchmarks, comparing against 18 widely-known comparative approaches. The results show that Inf-FS behaves better in almost any situation, that is, when the number of features to keep are fixed a priori, or when the decision of the subset cardinality is part of the process.