Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification
This is an incremental comparison of two existing methods adapted for multi-label classification, relevant for researchers in multi-label learning.
The paper compared the soft confusion matrix approach and Bayes metaclassifier for multi-label classification, finding that both methods performed similarly across 29 datasets and 11 quality criteria.
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.