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.

A Multi-objective evolutionary fuzzy system for big data

MARCELLONI, FRANCESCO;SEGATORI, ARMANDO
2016-01-01

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/826816
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