Optimizing Partition Granularity, Membership Function Parameters, and Rule Bases of Fuzzy Classifiers for Big Data by a Multi-objective Evolutionary Approach

  • Marco Barsacchi
  • Alessio BechiniEmail author
  • Pietro Ducange
  • Francesco Marcelloni


Classical data mining algorithms are considered inadequate to manage the volume, variety, velocity, and veracity aspects of big data. The advent of a number of open-source cluster-computing frameworks has opened new interesting perspectives for handling the volume and velocity features. In this context, thanks to their capability of coping with vague and imprecise information, distributed fuzzy models appear to be particularly suitable for handling the variety and veracity features of big data. Moreover, the interpretability of fuzzy models may assume a particular relevance in the context of big data mining. In this work, we propose a novel approach for generating, out of big data, a set of fuzzy rule–based classifiers characterized by different optimal trade-offs between accuracy and interpretability. We extend a state-of-the-art distributed multi-objective evolutionary learning scheme, implemented under the Apache Spark environment. In particular, we exploit a recently proposed distributed fuzzy decision tree learning approach for generating an initial rule base that serves as input to the evolutionary process. Furthermore, we integrate the evolutionary learning scheme with an ad hoc strategy for the granularity learning of the fuzzy partitions, along with the optimization of both the rule base and the fuzzy set parameters. Experimental investigations show that the proposed approach is able to generate fuzzy rule–based classifiers that are significantly less complex than the ones generated by the original multi-objective evolutionary learning scheme, while keeping the same accuracy levels.


Big data mining Multi-objective evolutionary fuzzy systems Fuzzy classification models Distributed algorithms 


Funding Information

This work was been partially supported by the University of Pisa under grant PRA_2017 “IoT e Big Data: metodologie e tecnologie per la raccolta e l’elaborazione di grosse moli di dati.” Moreover, the work carried out in implementing the described approach is part of the efforts for the development of the projects “SIBILLA” and “TALENT,” co-financed by Regione Toscana under the framework POR-FESR 2014-2020 - Bando 2.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with the active participation of humans. Furthermore, this article does not contain any studies on animals. The data collected and processed will be solely used for research related to this work and it will be ensured that they will not allow to identify any of the authors of such data.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversity of PisaPisaItaly
  2. 2.SMART Engineering Solutions, Technologies (SMARTEST) Research CentreeCAMPUS UniversityNovedrateItaly

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