Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution

Diagnosis of breast cancer disease depends on human experience. It is time consuming and has an element of human error in the results. This paper presents an intelligent multi-objective classifier to Diagnose breast cancer diseases using multilayer perceptron (MLP) neural network with Differential E...

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Main Authors:	Ibrahim, A. O., Shamsuddin, S. M., Saleh, A. Y., Abdelmaboud, A., Ali, A.
Format:	Conference or Workshop Item
Published:	Institute of Electrical and Electronics Engineers Inc. 2016
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/73470/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965148270&doi=10.1109%2fICCNEEE.2015.7381405&partnerID=40&md5=9ae1cb253a73c366f300951262d84afd
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Institution:	Universiti Teknologi Malaysia

id	my.utm.73470
record_format	eprints
spelling	my.utm.734702017-11-23T05:09:18Z http://eprints.utm.my/id/eprint/73470/ Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution Ibrahim, A. O. Shamsuddin, S. M. Saleh, A. Y. Abdelmaboud, A. Ali, A. QA75 Electronic computers. Computer science Diagnosis of breast cancer disease depends on human experience. It is time consuming and has an element of human error in the results. This paper presents an intelligent multi-objective classifier to Diagnose breast cancer diseases using multilayer perceptron (MLP) neural network with Differential Evolution technique. The Differential Evolution (DE) algorithm is used to solve multi-objective optimization problems by tuning MLP neural network parameters. The proposed intelligent multi-objective classifier is used for diagnosis of breast cancer disease. In addition, it utilizes the advantages of multi-objective differential evolution to optimize the number of hidden nodes in the hidden layer of the MLP neural network and also to reduce network error rate. The results indicate that the proposed intelligent multi-objective classifier is viable in breast cancer diagnosis. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item PeerReviewed Ibrahim, A. O. and Shamsuddin, S. M. and Saleh, A. Y. and Abdelmaboud, A. and Ali, A. (2016) Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution. In: 1st International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, ICCNEEE 2015, 7-9 Sept 2015, Khartoum, Sudan. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965148270&doi=10.1109%2fICCNEEE.2015.7381405&partnerID=40&md5=9ae1cb253a73c366f300951262d84afd
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Ibrahim, A. O. Shamsuddin, S. M. Saleh, A. Y. Abdelmaboud, A. Ali, A. Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution
description	Diagnosis of breast cancer disease depends on human experience. It is time consuming and has an element of human error in the results. This paper presents an intelligent multi-objective classifier to Diagnose breast cancer diseases using multilayer perceptron (MLP) neural network with Differential Evolution technique. The Differential Evolution (DE) algorithm is used to solve multi-objective optimization problems by tuning MLP neural network parameters. The proposed intelligent multi-objective classifier is used for diagnosis of breast cancer disease. In addition, it utilizes the advantages of multi-objective differential evolution to optimize the number of hidden nodes in the hidden layer of the MLP neural network and also to reduce network error rate. The results indicate that the proposed intelligent multi-objective classifier is viable in breast cancer diagnosis.
format	Conference or Workshop Item
author	Ibrahim, A. O. Shamsuddin, S. M. Saleh, A. Y. Abdelmaboud, A. Ali, A.
author_facet	Ibrahim, A. O. Shamsuddin, S. M. Saleh, A. Y. Abdelmaboud, A. Ali, A.
author_sort	Ibrahim, A. O.
title	Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution
title_short	Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution
title_full	Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution
title_fullStr	Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution
title_full_unstemmed	Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution
title_sort	intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2016
url	http://eprints.utm.my/id/eprint/73470/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965148270&doi=10.1109%2fICCNEEE.2015.7381405&partnerID=40&md5=9ae1cb253a73c366f300951262d84afd
_version_	1643656669596483584

Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution

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