Guiding fuzzy rule interpolation with information gains

Fangyi Li, Changjing Shang, Ying Li, Qiang Shen

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

Fuzzy rule interpolation enables fuzzy systems to perform inference with a sparse rule base. However, common approaches to fuzzy rule interpolation assume that rule antecedents are of equal significance while searching for rules to implement interpolation. As such, inaccurate or incorrect interpolated results may be produced. To help minimise the adverse impact of the equal significance assumption, this paper presents a novel approach for rule interpolation where information gain is utilised to evaluate the relative significance of rule antecedents in a given rule base. The approach is enabled by the introduction of an innovative reverse engineering technique that artificially creates training data from a given sparse rule base. The resulting method facilitates informed choice of most appropriate rules to compute interpolation. The work is implemented for scale and move transformation-based fuzzy rule interpolation, but the underlying idea can be extended to other rule interpolation methods. Comparative experimental evaluation demonstrates the efficacy of the proposed approach.

源语言英语
主期刊名Advances in Computational Intelligence Systems - Contributions Presented at the 16th UK Workshop on Computational Intelligence, 2016
编辑Alexander Gegov, Chrisina Jayne, Qiang Shen, Plamen Angelov
出版商Springer Verlag
165-183
页数19
ISBN(印刷版)9783319465616
DOI
出版状态已出版 - 2017
活动16th UK Workshop on Computational Intelligence, UKCI 2016 - Lancaster, 英国
期限: 7 9月 20169 9月 2016

出版系列

姓名Advances in Intelligent Systems and Computing
513
ISSN(印刷版)2194-5357

会议

会议16th UK Workshop on Computational Intelligence, UKCI 2016
国家/地区英国
Lancaster
时期7/09/169/09/16

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