Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification

TL;DR

This paper compares the soft confusion matrix approach and Bayes metaclassifier methods in multi-label classification, analyzing their performance across multiple datasets and criteria.

Contribution

It provides the first direct comparison of these two methods within the multi-label classification framework, using extensive empirical evaluation.

Findings

Both methods are effective in multi-label classification.

The comparison reveals performance differences depending on datasets and criteria.

Statistical analysis confirms significant performance variations.

Abstract

The aim of this paper was to compare soft confusion matrix approach and Bayes metaclassifier under the multi-label classification framework. Although the methods were successfully applied under the multi-label classification framework, they have not been compared directly thus far. Such comparison is of vital importance because both methods are quite similar as they are both based on the concept of randomized reference classifier. Since both algorithms were designed to deal with single-label problems, they are combined with the problem-transformation approach to multi-label classification. Present study included 29 benchmark datasets and four different base classifiers. The algorithms were compared in terms of 11 quality criteria and the results were subjected to statistical analysis.