Evolutionary Design of Linguistic Fuzzy Regression Systems with Adaptive Defuzzification in Big Data Environments

This paper is positioned in the area of the use of cognitive computation techniques to design intelligent systems for big data scenarios, specifically the use of evolutionary algorithms to design data-driven linguistic fuzzy rule-based systems for regression and control. On the one hand, data-driven approaches have been extensively employed to create rule bases for fuzzy regression and control from examples. On the other, adaptive defuzzification is a well-known mechanism used to significantly improve the accuracy of fuzzy systems. When dealing with large-scale scenarios, the aforementioned methods must be redesigned to allow scalability. Our proposal is based on a distributed MapReduce schema, relying on two ideas: first, a simple adaptation of a classic data-driven method to quickly obtain a set of rules, and, second, a novel scalable strategy that uses evolutionary adaptive defuzzification to achieve better behavior through cooperation among rules. Some different regression problems were used to validate our methodology through an experimental study developed and included at the end of our paper. Therefore, the proposed approach allows scalability while tackling applications of linguistic fuzzy rule-based systems for regression with adaptive defuzzification in large-scale data scenarios. This paper thus examines the use of some relevant techniques for cognitive computing when working with a vast volume of examples, a common occurrence when dealing with the design of artificial intelligent systems that perform reasoning in a similar way as humans.