Improved Multi-label Classification with Frequent Label-set Mining and Association

TL;DR

This paper introduces a novel method that leverages frequent label-set mining and association rules to exploit class correlations in multi-label data, enhancing classifier performance by refining soft scores based on co-occurrence and co-absence patterns.

Contribution

It proposes a new approach to incorporate class correlation information into existing multi-label classifiers using CP-CA rules, improving their accuracy.

Findings

Significant performance improvements on ten datasets.

Effective use of co-presence and co-absence information.

Enhanced classification accuracy across multiple classifiers.

Abstract

Multi-label (ML) data deals with multiple classes associated with individual samples at the same time. This leads to the co-occurrence of several classes repeatedly, which indicates some existing correlation among them. In this article, the correlation among classes has been explored to improve the classification performance of existing ML classifiers. A novel approach of frequent label-set mining has been proposed to extract these correlated classes from the label-sets of the data. Both co-presence (CP) and co-absence (CA) of classes have been taken into consideration. The rules mined from the ML data has been further used to incorporate class correlation information into existing ML classifiers. The soft scores generated by an ML classifier are modified through a novel approach using the CP-CA rules. A concept of certain and uncertain scores has been defined here, where the proposed method aims to improve the uncertain scores with the help of the certain scores and their corresponding CP-CA rules. This has been experimentally analysed on ten ML datasets for three ML existing classifiers which shows substantial improvement in their overall performance.