Curvature-Based Sparse Rule Base Generation for Fuzzy Interpolation Using Menger Curvature

Zheming Zuo, Jie Li, Longzhi Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fuzzy interpolation improves the applicability of fuzzy inference by allowing the utilisation of sparse rule bases. Curvature-based rule base generation approach has been recently proposed to support fuzzy interpolation. Despite the ability to directly generating sparse rule bases from data, the approach often suffers from the high dimensionality of complex inference problems. In this work, a different curvature calculation approach, i.e., the Menger approach, is employed to the curvature-based rule base generation approach in an effort to address the limitation. The experimental results confirm better efficiency and efficacy of the proposed method in generating rule bases on high-dimensional datasets.

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019
EditorsZhaojie Ju, Dalin Zhou, Alexander Gegov, Longzhi Yang, Chenguang Yang
PublisherSpringer-Verlag
Pages53-65
Number of pages13
ISBN (Print)9783030299323
DOIs
Publication statusPublished - 31 Jan 2020
Event19th Annual UK Workshop on Computational Intelligence, UKCI 2019 - Portsmouth, United Kingdom
Duration: 4 Sep 20196 Sep 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1043
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference19th Annual UK Workshop on Computational Intelligence, UKCI 2019
CountryUnited Kingdom
CityPortsmouth
Period4/09/196/09/19

Fingerprint

Interpolation
Fuzzy inference

Cite this

Zuo, Z., Li, J., & Yang, L. (2020). Curvature-Based Sparse Rule Base Generation for Fuzzy Interpolation Using Menger Curvature. In Z. Ju, D. Zhou, A. Gegov, L. Yang, & C. Yang (Eds.), Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019 (pp. 53-65). (Advances in Intelligent Systems and Computing; Vol. 1043). Springer-Verlag. https://doi.org/10.1007/978-3-030-29933-0_5
Zuo, Zheming ; Li, Jie ; Yang, Longzhi. / Curvature-Based Sparse Rule Base Generation for Fuzzy Interpolation Using Menger Curvature. Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. editor / Zhaojie Ju ; Dalin Zhou ; Alexander Gegov ; Longzhi Yang ; Chenguang Yang. Springer-Verlag, 2020. pp. 53-65 (Advances in Intelligent Systems and Computing).
@inproceedings{f2fbcc7daab445fdaacfea0b4053f899,
title = "Curvature-Based Sparse Rule Base Generation for Fuzzy Interpolation Using Menger Curvature",
abstract = "Fuzzy interpolation improves the applicability of fuzzy inference by allowing the utilisation of sparse rule bases. Curvature-based rule base generation approach has been recently proposed to support fuzzy interpolation. Despite the ability to directly generating sparse rule bases from data, the approach often suffers from the high dimensionality of complex inference problems. In this work, a different curvature calculation approach, i.e., the Menger approach, is employed to the curvature-based rule base generation approach in an effort to address the limitation. The experimental results confirm better efficiency and efficacy of the proposed method in generating rule bases on high-dimensional datasets.",
author = "Zheming Zuo and Jie Li and Longzhi Yang",
year = "2020",
month = "1",
day = "31",
doi = "10.1007/978-3-030-29933-0_5",
language = "English",
isbn = "9783030299323",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer-Verlag",
pages = "53--65",
editor = "Zhaojie Ju and Dalin Zhou and Alexander Gegov and Longzhi Yang and Chenguang Yang",
booktitle = "Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019",

}

Zuo, Z, Li, J & Yang, L 2020, Curvature-Based Sparse Rule Base Generation for Fuzzy Interpolation Using Menger Curvature. in Z Ju, D Zhou, A Gegov, L Yang & C Yang (eds), Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. Advances in Intelligent Systems and Computing, vol. 1043, Springer-Verlag, pp. 53-65, 19th Annual UK Workshop on Computational Intelligence, UKCI 2019, Portsmouth, United Kingdom, 4/09/19. https://doi.org/10.1007/978-3-030-29933-0_5

Curvature-Based Sparse Rule Base Generation for Fuzzy Interpolation Using Menger Curvature. / Zuo, Zheming; Li, Jie; Yang, Longzhi.

Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. ed. / Zhaojie Ju; Dalin Zhou; Alexander Gegov; Longzhi Yang; Chenguang Yang. Springer-Verlag, 2020. p. 53-65 (Advances in Intelligent Systems and Computing; Vol. 1043).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Curvature-Based Sparse Rule Base Generation for Fuzzy Interpolation Using Menger Curvature

AU - Zuo, Zheming

AU - Li, Jie

AU - Yang, Longzhi

PY - 2020/1/31

Y1 - 2020/1/31

N2 - Fuzzy interpolation improves the applicability of fuzzy inference by allowing the utilisation of sparse rule bases. Curvature-based rule base generation approach has been recently proposed to support fuzzy interpolation. Despite the ability to directly generating sparse rule bases from data, the approach often suffers from the high dimensionality of complex inference problems. In this work, a different curvature calculation approach, i.e., the Menger approach, is employed to the curvature-based rule base generation approach in an effort to address the limitation. The experimental results confirm better efficiency and efficacy of the proposed method in generating rule bases on high-dimensional datasets.

AB - Fuzzy interpolation improves the applicability of fuzzy inference by allowing the utilisation of sparse rule bases. Curvature-based rule base generation approach has been recently proposed to support fuzzy interpolation. Despite the ability to directly generating sparse rule bases from data, the approach often suffers from the high dimensionality of complex inference problems. In this work, a different curvature calculation approach, i.e., the Menger approach, is employed to the curvature-based rule base generation approach in an effort to address the limitation. The experimental results confirm better efficiency and efficacy of the proposed method in generating rule bases on high-dimensional datasets.

UR - http://www.scopus.com/inward/record.url?scp=85072863514&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-29933-0_5

DO - 10.1007/978-3-030-29933-0_5

M3 - Conference contribution

AN - SCOPUS:85072863514

SN - 9783030299323

T3 - Advances in Intelligent Systems and Computing

SP - 53

EP - 65

BT - Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019

A2 - Ju, Zhaojie

A2 - Zhou, Dalin

A2 - Gegov, Alexander

A2 - Yang, Longzhi

A2 - Yang, Chenguang

PB - Springer-Verlag

ER -

Zuo Z, Li J, Yang L. Curvature-Based Sparse Rule Base Generation for Fuzzy Interpolation Using Menger Curvature. In Ju Z, Zhou D, Gegov A, Yang L, Yang C, editors, Advances in Computational Intelligence Systems - Contributions Presented at the 19th UK Workshop on Computational Intelligence, 2019. Springer-Verlag. 2020. p. 53-65. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-29933-0_5