Ca-MCF: Category-level Multi-label Causal Feature selection

TL;DR

Ca-MCF is a novel method for multi-label causal feature selection that models causal mechanisms at the category level, improving accuracy and reducing feature redundancy in real-world datasets.

Contribution

It introduces category-level causal modeling with label flattening and a competition-based recovery mechanism using mutual information, advancing multi-label feature selection techniques.

Findings

Outperforms state-of-the-art benchmarks in accuracy

Reduces feature dimensionality effectively

Demonstrates robustness across diverse datasets

Abstract

Multi-label causal feature selection has attracted extensive attention in recent years. However, current methods primarily operate at the label level, treating each label variable as a monolithic entity and overlooking the fine-grained causal mechanisms unique to individual categories. To address this, we propose a Category-level Multi-label Causal Feature selection method named Ca-MCF. Ca-MCF utilizes label category flattening to decompose label variables into specific category nodes, enabling precise modeling of causal structures within the label space. Furthermore, we introduce an explanatory competition-based category-aware recovery mechanism that leverages the proposed Specific Category-Specific Mutual Information (SCSMI) and Distinct Category-Specific Mutual Information (DCSMI) to salvage causal features obscured by label correlations. The method also incorporates structural symmetry checks and cross-dimensional redundancy removal to ensure the robustness and compactness of the identified Markov Blankets. Extensive experiments across seven real-world datasets demonstrate that Ca-MCF significantly outperforms state-of-the-art benchmarks, achieving superior predictive accuracy with reduced feature dimensionality.