Title: A Correction Method of a Binary Classifier Applied to Multi-label Pairwise Models,
Abstract: In this work, we addressed the issue of applying a stochastic classifier and
a local, fuzzy confusion matrix under the framework of multi-label
classification. We proposed a novel solution to the problem of correcting label
pairwise ensembles. The main step of the correction procedure is to compute
classifier- specific competence and cross-competence measures, which estimates
error pattern of the underlying classifier. We considered two improvements of
the method of obtaining confusion matrices. The first one is aimed to deal with
imbalanced labels. The other utilizes double labelled instances which are
usually removed during the pairwise transformation. The proposed methods were
evaluated using 29 benchmark datasets. In order to assess the efficiency of the
introduced models, they were compared against 1 state-of-the-art approach and
the correction scheme based on the original method of confusion matrix
estimation. The comparison was performed using four different multi-label
evaluation measures: macro and micro-averaged F1 loss, zero-one loss and
Hamming loss. Additionally, we investigated relations between classification
quality, which is expressed in terms of different quality criteria, and
characteristics of multi-label datasets such as average imbalance ratio or
label density. The experimental study reveals that the correction approaches
significantly outperforms the reference method only in terms of zero-one loss.