Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix
This work addresses the issue of imbalanced data in multi-label classification for researchers and practitioners, though it appears incremental as it builds on existing fuzzy confusion matrix methods.
The authors tackled the problem of correcting label pairwise ensembles in multi-label classification by proposing a weighting scheme based on a fuzzy confusion matrix, which eliminates vulnerability to imbalanced class distribution and achieves satisfying classification quality across six criteria.
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.