A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification
In this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extr...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
2010
|
| Subjects: |
| _version_ | 1825720570092716032 |
|---|---|
| author | Quteishat, A. Lim, C.P. Tan, K.S. |
| author_facet | Quteishat, A. Lim, C.P. Tan, K.S. |
| author_sort | Quteishat, A. |
| collection | UM |
| description | In this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extracted from the modified FMM classifier, and a ``don't care'' approach is adopted by the GA rule extractor to minimize the number of features in the extracted rules. Five benchmark problems and a real medical diagnosis task are used to empirically evaluate the effectiveness of the proposed FMM-GA system. The results are analyzed and compared with other published results. In addition, the bootstrap hypothesis analysis is conducted to quantify the results of the medical diagnosis task statistically. The outcomes reveal the efficacy of FMM-GA in extracting a set of compact and yet easily comprehensible rules while maintaining a high classification performance for tackling pattern classification tasks. |
| first_indexed | 2024-03-06T05:37:07Z |
| format | Article |
| id | um.eprints-14712 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:37:07Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | um.eprints-147122015-11-11T01:50:13Z http://eprints.um.edu.my/14712/ A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification Quteishat, A. Lim, C.P. Tan, K.S. Q Science (General) In this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extracted from the modified FMM classifier, and a ``don't care'' approach is adopted by the GA rule extractor to minimize the number of features in the extracted rules. Five benchmark problems and a real medical diagnosis task are used to empirically evaluate the effectiveness of the proposed FMM-GA system. The results are analyzed and compared with other published results. In addition, the bootstrap hypothesis analysis is conducted to quantify the results of the medical diagnosis task statistically. The outcomes reveal the efficacy of FMM-GA in extracting a set of compact and yet easily comprehensible rules while maintaining a high classification performance for tackling pattern classification tasks. 2010 Article PeerReviewed Quteishat, A. and Lim, C.P. and Tan, K.S. (2010) A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification. IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans, 40 (3). pp. 641-650. |
| spellingShingle | Q Science (General) Quteishat, A. Lim, C.P. Tan, K.S. A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification |
| title | A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification |
| title_full | A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification |
| title_fullStr | A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification |
| title_full_unstemmed | A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification |
| title_short | A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification |
| title_sort | modified fuzzy min max neural network with a genetic algorithm based rule extractor for pattern classification |
| topic | Q Science (General) |
| work_keys_str_mv | AT quteishata amodifiedfuzzyminmaxneuralnetworkwithageneticalgorithmbasedruleextractorforpatternclassification AT limcp amodifiedfuzzyminmaxneuralnetworkwithageneticalgorithmbasedruleextractorforpatternclassification AT tanks amodifiedfuzzyminmaxneuralnetworkwithageneticalgorithmbasedruleextractorforpatternclassification AT quteishata modifiedfuzzyminmaxneuralnetworkwithageneticalgorithmbasedruleextractorforpatternclassification AT limcp modifiedfuzzyminmaxneuralnetworkwithageneticalgorithmbasedruleextractorforpatternclassification AT tanks modifiedfuzzyminmaxneuralnetworkwithageneticalgorithmbasedruleextractorforpatternclassification |