A Modified Fuzzy Min–Max Neural Network With a Genetic-Algorithm-Based Rule Extractor for Pattern Classification

Quteishat, Anas; Lim, Chee Peng; Tan, Kay Sin

doi:10.1109/tsmca.2010.2043948

Cited by 80 publications

(23 citation statements)

References 33 publications

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“…[16]. Properties of this system include-a pruning procedure to eliminate hyperboxes with low confidence factors, a GA rule extractor to generate and select a compact rule set with high classification accuracy and a rule extraction procedure to extract fuzzy if-then rules with "don't care" antecedents.…”

Section: A Medical Diagnosismentioning

confidence: 99%

Thyroid disease diagnosis: A survey

Shroff¹,

Pise²,

Chalekar³

et al. 2015

2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO)

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Section: A Medical Diagnosismentioning

confidence: 99%

Thyroid disease diagnosis: A survey

Shroff¹,

Pise²,

Chalekar³

et al. 2015

2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO)

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“…The rough-fuzzy computing provides a powerful mathematical framework to capture uncertainties associated with the data. Other hybridized models for pattern recognition and data mining include neurogenetic [46, 215,251,293,362], rough-genetic [43, 317,369], fuzzy-genetic [14,61,64,116,180,233], rough-neuro-genetic [167], rough-neuro-fuzzy [11,24,243,244,262], and neuro-fuzzy-genetic [187,195,283,290,307,358] approaches.…”

Section: Relevance Of Soft Computingmentioning

confidence: 99%

Introduction to Pattern Recognition and Bioinformatics

Maji

Paul

2014

Scalable Pattern Recognition Algorithms

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“…A rule with huge number of anteced-ents is not a desirable feature. Rule extraction technique is only considered useful when only the significant attributes are retained as a result of extraction [27]. A solution of overcoming the problem associated with a large number of rule antecedents was proposed in [23].…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm Based Fuzzy Inference System for Pattern Classification and Rule Extraction

Wong

Yap

2018

IJET

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Setting fuzzy rules is one of the paramount techniques in the design of a fuzzy system. For a simple system, fuzzy if-then rules are usually derived from the human experts. However, in the event of having multiple variables coupled with a few features, the classification problem will be getting more sophisticated, as a result human expert may not be able to derive proper rules. This paper presents a genetic-algorithm-based fuzzy inference system for extracting highly comprehensible fuzzy rules to be implemented in human practices without detailed computation (hereafter denoted as GA-FIS). The impetus for developing a new and efficient GA-FIS model arises from the need of constructing fuzzy rules directly from raw data sets that combines good approximation and classification properties with compactness and transparency. Therefore, our proposed GA-FIS method will first define the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then genetic algorithm serves as an optimization tool to construct the best combination of rules in fuzzy inference system that can achieve higher classification accuracy and gain better interpretability. The proposed approach is applied to various benchmark and real world problems and the results show its validity.

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A Modified Fuzzy Min–Max Neural Network With a Genetic-Algorithm-Based Rule Extractor for Pattern Classification

Cited by 80 publications

References 33 publications

Thyroid disease diagnosis: A survey

Thyroid disease diagnosis: A survey

Introduction to Pattern Recognition and Bioinformatics

A Genetic Algorithm Based Fuzzy Inference System for Pattern Classification and Rule Extraction

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