An extraction method for the characterization of the Fuzzy Rule Based Classification Systems´ behavior using data complexity measures: A case of study with FH-GBML

When dealing with problems using Fuzzy Rule Based Classification Systems it is difficult to know in advance whether the model will perform well or badly. In this work we present an automatic extraction method to determine the domains of competence of Fuzzy Rule Based Classification Systems As a case of study we use the Fuzzy Hybrid Genetic Based Machine Learning method. We consider twelve metrics of data complexity in order to analyze the behavior patterns of this method, obtaining intervals of such data complexity measures with good or bad performance of it. Combining these intervals we obtain rules that describe both good or bad behaviors of the Fuzzy Rule Based Classification System mentioned. These rules allow describe both good or bad behaviors of the Fuzzy Rule Based Classification Systems mentioned, allowing us to characterize the response quality of the methods from the data set complexity metrics of a given data set. Thus, we can establish the domains of competence of the Fuzzy Rule Based Classification Systems considered, making it possible to establish when the method will perform well or badly prior to its application.