Inductive Learning of Fuzzy Rule-Based Classifier with Self-Constructing Clustering

The inductive learning of fuzzy rule-based classification systems usually encounters an issue of exponential growth of the fuzzy rulesearch space when the number of patternsand/or variables becomes large.This issue makes the learning process more difficultand, in most cases, may lead to scalability problems. Alcalá-Fdezet al.proposed a fuzzy association rule-based classification method for high-dimensional problems, whichis based on three stages to obtain an accurate and compact fuzzyrule-based classifier with a low computational cost.But there is a serious drawback with this method: the initial linguistic termsmust be predefined by the user. Weapply a self-constructing clustering technique for determining the linguistic terms automatically according to thecharacteristics of the training data. Therefore, the resulting classification system can be more friendly and time-saving for use to the user. Furthermore, more accurate classification results can usually be obtained for the user.