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Subject Item

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bibo:AcademicArticle n15:EPrint bibo:Article n15:ArticleEPrint

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n9:j.ins.2018.03.004

dcterms:title

Self-organising fuzzy logic classifier

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dcterms:date

2018-06-01

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n11:ext-x.gu@kent.ac.uk n11:ext-f8571a00eeea2a25985c7ba8ba56dbd5

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n16:peerReviewed n16:published

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n10:ext-f308aad1ef8f70546c3a197f104f2ad5

bibo:abstract

In this paper, we present a self-organising nonparametric fuzzy rule-based classifier. The proposed approach identifies prototypes from the observed data through an offline training process and uses them to build a 0-order AnYa type fuzzy rule-based system for classification. Once primed offline, it is able to continuously learn from the streaming data afterwards to follow the changing data pattern by updating the system structure and meta-parameters recursively. The meta-parameters of the proposed approach are derived from data directly. By changing the level of granularity, the proposed approach can make a trade-off between performance and computational efficiency, and, thus, the classifier is able to address a wide variety of problems with specific needs. The classifier also supports different types of distance measures. Numerical examples based on benchmark datasets demonstrate the high performance of the proposed approach and its ability of handling high-dimensional, complex, large-scale problems.

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n12:ext-00200255 n13:repository

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n4:QA75

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n14:authors

bibo:volume

447