A Correction Method of a Binary Classifier Applied to Multi-Label Pairwise Models

In the paper, the problem of multi-label (ML) classification using the label-pairwise (LPW) scheme is addressed. For this approach, the method of correction of binary classifiers which constitute the LPW ensemble is proposed. The correction is based on a probabilistic (randomized) model of a classifier that assesses the local class-specific probabilities of correct classification and misclassification. These probabilities are determined using the original concepts of a randomized reference classifier (RRC) and a local soft confusion matrix. Additionally, two special cases that deal with imbalanced labels and double labeled instances are considered. The proposed methods were evaluated using 29 benchmark datasets. In order to assess the efficiency of the introduced models and the proposed correction scheme, they were compared against original binary classifiers working in the LPW ensemble. The comparison was performed using four different ML evaluation measures: macro and micro-averaged [Formula: see text] loss, zero-one loss and Hamming loss. Moreover, relations between classification quality and the characteristics of ML datasets such as average imbalance ratio or label density were investigated. The experimental study reveals that the correction approaches significantly outperform the reference method in terms of zero-one loss and Hamming loss.