Abstract
•we carefully analyzed the concept of fuzzy classifiers elaborating on their functional modules and their design process.•we present a thorough generalization of fuzzy classifiers by involving various t-norms and t-conorms and compare the performance of different construct strategies.•we propose an augmentation method for the fuzzy classifier by introducing an interaction to quantify the strength of connection between the fuzzy rules and membership.•we contrast the quality of fuzzy classifiers with those commonly non-fuzzy classifiers and identify situations where fuzzy sets used in classifiers outperforms their counterparts.
Fuzzy classifiers have been studied in the area of fuzzy sets for a long time resulting in a number of architectures. In this study, we thoroughly investigate and critically assess fuzzy rule-based classifiers. A topology of the classifier is discussed along with a discussion of the role of fuzzy set technology in the construction of condition and conclusion parts of the classification rules. Some optimization mechanisms utilized in the adjustment of information granules forming the rules are presented. Performance of the fuzzy classifiers is quantified in terms of their accuracy and an area under curve (AUC) determined for the receiver operating characteristics (ROC). The performance of the classifier is evaluated vis-à-vis a collection of triangular norms used in the construction of the fuzzy classifiers. Experimental studies involve synthetic and publicly available data. Furthermore, comparative studies include the experiments with the commonly used non-fuzzy classifiers.