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Empirical Fuzzy Sets and Systems

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

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Empirical Fuzzy Sets and Systems. / Angelov, P.P.; Gu, X.

Empirical Approach to Machine Learning. ed. / Plamen Angelov; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. p. 135-155 (Studies in Computational Intelligence; Vol. 800).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Harvard

Angelov, PP & Gu, X 2019, Empirical Fuzzy Sets and Systems. in P Angelov & X Gu (eds), Empirical Approach to Machine Learning. vol. 800, Studies in Computational Intelligence, vol. 800, Springer-Verlag, pp. 135-155. https://doi.org/10.1007/978-3-030-02384-3_5

APA

Angelov, P. P., & Gu, X. (2019). Empirical Fuzzy Sets and Systems. In P. Angelov, & X. Gu (Eds.), Empirical Approach to Machine Learning (Vol. 800, pp. 135-155). (Studies in Computational Intelligence; Vol. 800). Springer-Verlag. https://doi.org/10.1007/978-3-030-02384-3_5

Vancouver

Angelov PP, Gu X. Empirical Fuzzy Sets and Systems. In Angelov P, Gu X, editors, Empirical Approach to Machine Learning. Vol. 800. Springer-Verlag. 2019. p. 135-155. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-02384-3_5

Author

Angelov, P.P. ; Gu, X. / Empirical Fuzzy Sets and Systems. Empirical Approach to Machine Learning. editor / Plamen Angelov ; Xiaowei Gu. Vol. 800 Springer-Verlag, 2019. pp. 135-155 (Studies in Computational Intelligence).

Bibtex

@inbook{04610d15730c44249d0ee6102d216f57,
title = "Empirical Fuzzy Sets and Systems",
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. {\textcopyright} 2019, Springer Nature Switzerland AG.",
author = "P.P. Angelov and X. Gu",
year = "2019",
doi = "10.1007/978-3-030-02384-3_5",
language = "English",
isbn = "9783030023836",
volume = "800",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag",
pages = "135--155",
editor = "Plamen Angelov and Xiaowei Gu",
booktitle = "Empirical Approach to Machine Learning",

}

RIS

TY - CHAP

T1 - Empirical Fuzzy Sets and Systems

AU - Angelov, P.P.

AU - Gu, X.

PY - 2019

Y1 - 2019

N2 - 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. © 2019, Springer Nature Switzerland AG.

AB - 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. © 2019, Springer Nature Switzerland AG.

U2 - 10.1007/978-3-030-02384-3_5

DO - 10.1007/978-3-030-02384-3_5

M3 - Chapter (peer-reviewed)

SN - 9783030023836

VL - 800

T3 - Studies in Computational Intelligence

SP - 135

EP - 155

BT - Empirical Approach to Machine Learning

A2 - Angelov, Plamen

A2 - Gu, Xiaowei

PB - Springer-Verlag

ER -