A novel and efficient neuro-fuzzy classifier for medical diagnosis

Chin Ming Hong; Chih Ming Chen; Shyuan Yi Chen; Chao Yen Huang

A novel and efficient neuro-fuzzy classifier for medical diagnosis

Chin Ming Hong^*, Chih Ming Chen, Shyuan Yi Chen, Chao Yen Huang

^*此作品的通信作者

電機工程學系

研究成果: 書貢獻/報告類型 › 會議論文篇章

11 引文斯高帕斯（Scopus）

摘要

This study attempts to propose a novel neuro-fuzzy network which can efficiently reason fuzzy rules based on training data to solve the medical diagnosis problems. First, this study proposes a refined K-means clustering algorithm and a gradient-based learning rules to logically determine and adaptively tuned the fuzzy membership functions for the employed neuro-fuzzy network. In the meanwhile, this study also presents a feature reduction scheme based on the grey-relational analysis to simplify the fuzzy rules obtained from the employed neuro-fuzzy network. Experimental results indicated that the proposed neuro-fuzzy network with feature reduction can discover very simplified and easily interpretable fuzzy rules to support medical diagnosis.

原文	英語
主出版物標題	International Joint Conference on Neural Networks 2006, IJCNN '06
頁面	735-741
頁數	7
出版狀態	已發佈 - 2006
事件	International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, 加拿大持續時間: 2006 7月 16 → 2006 7月 21

出版系列

名字	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN（列印）	1098-7576

其他

其他	International Joint Conference on Neural Networks 2006, IJCNN '06
國家/地區	加拿大
城市	Vancouver, BC
期間	2006/07/16 → 2006/07/21

ASJC Scopus subject areas

軟體

其它文件與鏈接

引用此

Hong, CM, Chen, CM, Chen, SY & Huang, CY 2006, A novel and efficient neuro-fuzzy classifier for medical diagnosis. 於 International Joint Conference on Neural Networks 2006, IJCNN '06., 1716168, IEEE International Conference on Neural Networks - Conference Proceedings, 頁 735-741, International Joint Conference on Neural Networks 2006, IJCNN '06, Vancouver, BC, 加拿大, 2006/07/16.

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title = "A novel and efficient neuro-fuzzy classifier for medical diagnosis",

abstract = "This study attempts to propose a novel neuro-fuzzy network which can efficiently reason fuzzy rules based on training data to solve the medical diagnosis problems. First, this study proposes a refined K-means clustering algorithm and a gradient-based learning rules to logically determine and adaptively tuned the fuzzy membership functions for the employed neuro-fuzzy network. In the meanwhile, this study also presents a feature reduction scheme based on the grey-relational analysis to simplify the fuzzy rules obtained from the employed neuro-fuzzy network. Experimental results indicated that the proposed neuro-fuzzy network with feature reduction can discover very simplified and easily interpretable fuzzy rules to support medical diagnosis.",

author = "Hong, {Chin Ming} and Chen, {Chih Ming} and Chen, {Shyuan Yi} and Huang, {Chao Yen}",

year = "2006",

language = "English",

isbn = "0780394909",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

pages = "735--741",

booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",

note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",

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T1 - A novel and efficient neuro-fuzzy classifier for medical diagnosis

AU - Hong, Chin Ming

AU - Chen, Chih Ming

AU - Chen, Shyuan Yi

AU - Huang, Chao Yen

PY - 2006

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N2 - This study attempts to propose a novel neuro-fuzzy network which can efficiently reason fuzzy rules based on training data to solve the medical diagnosis problems. First, this study proposes a refined K-means clustering algorithm and a gradient-based learning rules to logically determine and adaptively tuned the fuzzy membership functions for the employed neuro-fuzzy network. In the meanwhile, this study also presents a feature reduction scheme based on the grey-relational analysis to simplify the fuzzy rules obtained from the employed neuro-fuzzy network. Experimental results indicated that the proposed neuro-fuzzy network with feature reduction can discover very simplified and easily interpretable fuzzy rules to support medical diagnosis.

AB - This study attempts to propose a novel neuro-fuzzy network which can efficiently reason fuzzy rules based on training data to solve the medical diagnosis problems. First, this study proposes a refined K-means clustering algorithm and a gradient-based learning rules to logically determine and adaptively tuned the fuzzy membership functions for the employed neuro-fuzzy network. In the meanwhile, this study also presents a feature reduction scheme based on the grey-relational analysis to simplify the fuzzy rules obtained from the employed neuro-fuzzy network. Experimental results indicated that the proposed neuro-fuzzy network with feature reduction can discover very simplified and easily interpretable fuzzy rules to support medical diagnosis.

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M3 - Conference contribution

AN - SCOPUS:40649114556

SN - 0780394909

SN - 9780780394902

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

SP - 735

EP - 741

BT - International Joint Conference on Neural Networks 2006, IJCNN '06

T2 - International Joint Conference on Neural Networks 2006, IJCNN '06

Y2 - 16 July 2006 through 21 July 2006

ER -

A novel and efficient neuro-fuzzy classifier for medical diagnosis

摘要

出版系列

其他

ASJC Scopus subject areas

其它文件與鏈接

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