Efficient inference models for classification problems with a high number of fuzzy rules

Abstract

In data science there are problems that are not visible until you work with a sufficiently large number of data. This is the case, for example, with the design of the inference engine in fuzzy rule-based classification systems. The most common way to implement the winning rule inference method is to use sequential processing that reviews each of the rules in the rule set, to determine the best one and return the associated class. This implementation produces fast response times when the set of rules is small and is applied to a small set of examples. In this paper we explore new versions to implement this inference method, avoiding analyzing all the rules and focusing the analysis on the neighborhood of rules around the example. We study experimentally the conditions where each of them should be applied. Finally, we propose an implementation that combines all the studied versions offering good accuracy results and a significant reduction in the response time.