Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data

TL;DR

This paper introduces an unsupervised feature ranking method based on coalitional game theory that effectively reduces redundancy and maximizes information in categorical data without requiring labels.

Contribution

It proposes a novel redundancy-aware feature importance scoring method using coalitional game theory, improving unsupervised feature selection performance.

Findings

Outperforms existing methods in reducing feature redundancy

Maximizes information retention in selected features

Includes an efficient approximation of the algorithm

Abstract

Not all real-world data are labeled, and when labels are not available, it is often costly to obtain them. Moreover, as many algorithms suffer from the curse of dimensionality, reducing the features in the data to a smaller set is often of great utility. Unsupervised feature selection aims to reduce the number of features, often using feature importance scores to quantify the relevancy of single features to the task at hand. These scores can be based only on the distribution of variables and the quantification of their interactions. The previous literature, mainly investigating anomaly detection and clusters, fails to address the redundancy-elimination issue. We propose an evaluation of correlations among features to compute feature importance scores representing the contribution of single features in explaining the dataset's structure. Based on Coalitional Game Theory, our feature importance scores include a notion of redundancy awareness making them a tool to achieve redundancy-free feature selection. We show that the deriving features' selection outperforms competing methods in lowering the redundancy rate while maximizing the information contained in the data. We also introduce an approximated version of the algorithm to reduce the complexity of Shapley values' computations.