Reasoning Methods in Fuzzy Rule-based Classification Systems for Big Data Problems

Abstract

The analysis with a very high number of examples is a subject of growing interest that needs new algorithms and procedures. In this case, we study how the massive use of data affects the reasoning processes for classification problems that make use of fuzzy rule-based systems. First, we describe the standard reasoning model and the operations associated with its use, and once it is verified that these calculations may be inefficient in some cases we propose a new model to perform such calculations. Basically, the proposal eliminates the need to review all the rules in every inference process, generating the rule that best adapts to the particular example, which does not have to be part of the set of rules, and from it explore only the rules that have some effect on the example. We make an experimental study that shows the interest of the proposal presented.