Evolutionary Fuzzy System

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An Evolutionary Fuzzy System is a hybrid intelligent system that integrates a fuzzy systems with evolutionary algorithm.



References

2016

  • (Ferranti et al., 2016) ⇒ Ferranti, A., Marcelloni, F., & Segatori, A. (2016, July). A Multi-objective evolutionary fuzzy system for big data. In Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on (pp. 1562-1569). DOI:10.1109/FUZZ-IEEE.2016.7737876.
    • ABSTRACT: One of the most appealing features of fuzzy rule-based classifiers is the capability of explaining how the conclusions are inferred. This feature is hard to preserve when fuzzy rules are extracted from a very large amount of data. In this paper, we propose a distributed version of PAES-RCS, a multiobjective evolutionary approach to learn concurrently the rule and data bases of fuzzy rule-based classifiers by maximizing accuracy and minimizing complexity. PAES-RCS has proven to be very efficient in obtaining satisfactory approximations of the Pareto front exploiting a limited number of iterations. We implemented the distributed version of PAES-RCS by using Apache Spark as data processing framework. We discuss the effectiveness of our approach in terms of classification rate and scalability by performing a number of experiments on three real-world big datasets. Further, we compare our approach with other well-known state-of-art algorithms in terms of both accuracy and complexity, and evaluate the achievable speedup on a small computer cluster. We show that the distributed version can efficiently extract compact rule bases with high accuracy and allows handling big datasets even with modest hardware support.

2015

2011

2008

1999

  • (Shi et al., 1999) ⇒ Shi, Y., Eberhart, R., & Chen, Y. (1999). Implementation of evolutionary fuzzy systems. IEEE Transactions on fuzzy systems, 7(2), 109-119.
    • ABSTRACT: In this paper, evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionary algorithm are adapted via a fuzzy system. Benefits of the methodology are illustrated in the process of classifying the iris data set. Possible extensions of the methods are summarized.