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Empirical Approach to Machine Learning pp 135–155Cite as

Empirical Fuzzy Sets and Systems

Part of the Studies in Computational Intelligence book series (SCI,volume 800)

Abstract

In this chapter, the concepts and general principles of the empirical fuzzy sets and the fuzzy rule-based (FRB) systems based on them, named empirical FRB systems are presented, and two approaches for identifying empirical FRB systems, namely, the subjective one, which is based on human expertise, and the objective one, which is based on the autonomous data partitioning algorithm, are also presented. The traditional fuzzy sets and systems suffer from the so-called “curse of dimensionality”, they heavily rely on ad hoc decision and lack objectiveness. In contrast, the empirical approach to identify the empirical fuzzy sets and FRB systems effectively combine the data- and human-derived models and minimize the involvement of human expertise. They have significant advantages over the traditional ones because of the very strong interpretability, high objectiveness, being data driven and free from prior assumptions.

Keywords

  • Fuzzy Sets
  • Traditional Fuzzy Set
  • Human-derived Models
  • Strong Interpretability
  • Fuzzy Rule Base (FRB)

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  1. School of Computing and Communications, Lancaster University, Lancaster, UK

    Plamen P. Angelov & Xiaowei Gu

Authors
  1. Plamen P. Angelov
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  2. Xiaowei Gu
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Correspondence to Plamen P. Angelov .

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Angelov, P.P., Gu, X. (2019). Empirical Fuzzy Sets and Systems. In: Empirical Approach to Machine Learning. Studies in Computational Intelligence, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-02384-3_5

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