Feature ranking for multi-label classification using Markov Networks

TL;DR

This paper introduces a Markov Network-based method for feature ranking in multi-label classification, effectively modeling label-feature dependencies and outperforming traditional approaches in experiments.

Contribution

It presents a novel, efficient feature ranking approach using Markov Networks and the Ising model, with theoretical justification and practical advantages.

Findings

Outperforms conventional feature ranking methods

Provides interpretable dependency structures

Works efficiently on artificial and real datasets

Abstract

We propose a simple and efficient method for ranking features in multi-label classification. The method produces a ranking of features showing their relevance in predicting labels, which in turn allows to choose a final subset of features. The procedure is based on Markov Networks and allows to model the dependencies between labels and features in a direct way. In the first step we build a simple network using only labels and then we test how much adding a single feature affects the initial network. More specifically, in the first step we use the Ising model whereas the second step is based on the score statistic, which allows to test a significance of added features very quickly. The proposed approach does not require transformation of label space, gives interpretable results and allows for attractive visualization of dependency structure. We give a theoretical justification of the procedure by discussing some theoretical properties of the Ising model and the score statistic. We also discuss feature ranking procedure based on fitting Ising model using $l_1$ regularized logistic regressions. Numerical experiments show that the proposed methods outperform the conventional approaches on the considered artificial and real datasets.