Evolving Multi-label Classification Rules by Exploiting High-order Label Correlation

TL;DR

This paper introduces a method for multi-label classification that exploits high-order label correlations within subsets, improving prediction accuracy while addressing computational challenges, and demonstrates competitive results on benchmark datasets.

Contribution

It presents a novel approach combining high-order label correlation exploitation with label powerset and prediction aggregation, enhancing multi-label classification performance.

Findings

Competitive performance on benchmark datasets

Effective exploitation of high-order label correlations

Analysis of computational complexity

Abstract

In multi-label classification tasks, each problem instance is associated with multiple classes simultaneously. In such settings, the correlation between labels contains valuable information that can be used to obtain more accurate classification models. The correlation between labels can be exploited at different levels such as capturing the pair-wise correlation or exploiting the higher-order correlations. Even though the high-order approach is more capable of modeling the correlation, it is computationally more demanding and has scalability issues. This paper aims at exploiting the high-order label correlation within subsets of labels using a supervised learning classifier system (UCS). For this purpose, the label powerset (LP) strategy is employed and a prediction aggregation within the set of the relevant labels to an unseen instance is utilized to increase the prediction capability of the LP method in the presence of unseen labelsets. Exact match ratio and Hamming loss measures are considered to evaluate the rule performance and the expected fitness value of a classifier is investigated for both metrics. Also, a computational complexity analysis is provided for the proposed algorithm. The experimental results of the proposed method are compared with other well-known LP-based methods on multiple benchmark datasets and confirm the competitive performance of this method.