Exploring label correlations using decision templates for ensemble of classifier chains

TL;DR

This paper introduces UDDTECC, a novel ensemble method for multi-label classification that leverages label correlations through decision templates, improving performance over traditional fusion strategies.

Contribution

The work proposes Unconditionally Dependent Decision Templates for Ensemble of Classifier Chains, explicitly exploiting label dependencies to enhance multi-label classification accuracy.

Findings

UDDTECC outperforms traditional fusion methods on most metrics.

Exploiting label correlations improves classification performance.

Decision templates adaptation benefits ensemble of classifier chains.

Abstract

The use of ensemble-based multi-label methods has been shown to be effective in improving multi-label classification results. One of the most widely used ensemble-based multi-label classifiers is Ensemble of Classifier Chains. Decision templates for Ensemble of Classifier Chains (DTECC) is a fusion scheme based on Decision Templates that combines the predictions of Ensemble of Classifier Chains using information from the decision profile for each label, without considering information about other labels that might contribute to the classified result. Based on DTECC, this work proposes the Unconditionally Dependent Decision Templates for Ensemble of Classifier Chains (UDDTECC) method, a classifier fusion method that seeks to exploit correlations between labels in the fusion process. In this way, the classification of each label in the problem takes into account the label values that are considered conditionally dependent and that can lead to an improvement in the classification performance. The proposed method is experimentally compared with two traditional classifier fusion strategies and with a stacking-based strategy. Empirical evidence shows that using the proposed Decision Templates adaptation can improve the performance compared to the traditionally used fusion schemes on most of the evaluated metrics.