Title: Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix,
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. At the fusion phase we employed two
weighting approaches based on information theory. The classifier weights
promote base classifiers which are the most susceptible to the correction based
on the fuzzy confusion matrix. During the experimental study, the proposed
approach was compared against two reference methods. The comparison was made in
terms of six different quality criteria. The conducted experiments reveals that
the proposed approach eliminates one of main drawbacks of the original
FCM-based approach i.e. the original approach is vulnerable to the imbalanced
class/label distribution. What is more, the obtained results shows that the
introduced method achieves satisfying classification quality under all
considered quality criteria. Additionally, the impact of fluctuations of data
set characteristics is reduced.