Improved Multi-label Classification with Frequent Label-set Mining and Association
This work addresses multi-label classification by enhancing existing classifiers with correlation rules, offering an incremental improvement for applications with correlated labels.
The authors tackled the problem of multi-label classification by mining frequent label-sets to capture class correlations, including co-presence and co-absence, and used these to modify classifier scores, resulting in substantial performance improvements across ten datasets.
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.