Semantically Explainable Fuzzy Classifier

Abstract

In machine learning, there are many high-performance classifiers. However, because of lack of transparency, they are not able to explain the data in a human-friendly form. In this paper, Cumulative Fuzzy Class Membership Criterion (CFCMC), a recently proposed fuzzy modeling classifier, is modified and utilized for a novel approach of information extraction from the labeled data. This approach is able to explain the classifiability of the data in the form of semantics. Extracted semantics give information about the structure of the data and the similarities between classes. To get a relevant image of its classification performance, it is compared to three well-known and frequently used classifiers, which are considered as black boxes, namely, SVM, MLP, and kNN, and to a similar transparent approach, MF ARTMAP. To validate extracted semantics, they are compared to visualization of classified data and to confusion matrices generated during the evaluation of the created CFCMC models. The experimental result shows that CFCMC is not necessarily the best classifier, although, in most cases, it is not too far from the best performing methods. However, the semantical explanation potentially allows the classifier to be applied as a support for human decision processes in real-world problems.