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The aim of this paper was to compare soft confusion matrix approach and Bayesmetaclassifier under the multi-label classification framework. Although themethods were successfully applied under the multi-label classificationframework, they have not been compared directly thus far. Such comparison is ofvital importance because both methods are quite similar as they are both basedon the concept of randomized reference classifier. Since both algorithms weredesigned to deal with single-label problems, they are combined with theproblem-transformation approach to multi-label classification. Present studyincluded 29 benchmark datasets and four different base classifiers. Thealgorithms were compared in terms of 11 quality criteria and the results weresubjected to statistical analysis.

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