Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis
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article-4986 |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis</title><creator>Ibrahim, Ashraf Osman; Faculty of computer and technology, Alzaiem Alazhari University, Khartoum, Sudan</creator><creator>Shamsuddin, Siti Mariyam; UTM Big Data Centre, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia</creator><creator>Saleh, Abdulrazak Yahya; FSKPM Faculty, University Malaysia Sarawak (UNIMAS), Kota Samarahan, 94300 Sarawak, Malaysia</creator><creator>Ahmed, Ali; Faculty of computer science and Information Technology, Karary University,
Omdurman, 12305, Sudan</creator><creator>Ismail, Mohd Arfian; Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Pahang, Malaysia</creator><creator>Kasim, Shahreen; Soft Computing and Data Mining Centre, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn, Malaysia</creator><subject lang="en-US">local search; breast cancer; neural network; NSGA-II; ANN.</subject><description lang="en-US">The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to  show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm.</description><publisher lang="en-US">International Journal on Advanced Science, Engineering and Information Technology</publisher><contributor lang="en-US"/><date>2019-03-05</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Other:</type><type>File:application/pdf</type><identifier>http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/4986</identifier><identifier>10.18517/ijaseit.9.2.4986</identifier><source lang="en-US">International Journal on Advanced Science, Engineering and Information Technology; Vol 9, No 2 (2019); 609-615</source><source>2460-6952</source><source>2088-5334</source><language>eng</language><relation>http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/4986/pdf_1072</relation><rights lang="en-US">Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).</rights><recordID>article-4986</recordID></dc>
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| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other Other: File:application/pdf File Journal:eJournal |
| author |
Ibrahim, Ashraf Osman; Faculty of computer and technology, Alzaiem Alazhari University, Khartoum, Sudan Shamsuddin, Siti Mariyam; UTM Big Data Centre, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia Saleh, Abdulrazak Yahya; FSKPM Faculty, University Malaysia Sarawak (UNIMAS), Kota Samarahan, 94300 Sarawak, Malaysia Ahmed, Ali; Faculty of computer science and Information Technology, Karary University, Omdurman, 12305, Sudan Ismail, Mohd Arfian; Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Pahang, Malaysia Kasim, Shahreen; Soft Computing and Data Mining Centre, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn, Malaysia |
| title |
Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis |
| publisher |
International Journal on Advanced Science, Engineering and Information Technology |
| publishDate |
2019 |
| topic |
local search breast cancer neural network NSGA-II ANN |
| url |
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/4986 http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/4986/pdf_1072 |
| contents |
The role of intelligence techniques is becoming more significant in detecting and diagnosis of medical data. However, the performance of such methods is based on the algorithms or technique. In this paper, we develop an intelligent technique using multiobjective evolutionary method hybrid with a local search approach to enhance the backpropagation neural network. First, we enhance the famous multiobjective evolutionary algorithms, which is a non-dominated sorting genetic algorithm (NSGA-II). Then, we hybrid the enhanced algorithm with the local search strategy to ensures the acceleration of the convergence speed to the non-dominated front. In addition, such hybridization get the solutions achieved are well spread over it. As a result of using a local search method the quality of the Pareto optimal solutions are increased and all individuals in the population are enhanced. The key notion of the proposed algorithm was to show a new technique to settle automaticly artificial neural network design problem. The empirical results generated by the proposed intelligent technique evaluated by applying to the breast cancer dataset and emphasize the capability of the proposed algorithm to improve the results. The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm. |
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IOS1116.article-4986 |
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Indonesian Society for Knowledge and Human Development |
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204 |
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library:special library |
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Indonesian Society for Knowledge and Human Development |
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International Journal on Advanced Science, Engineering and Information Technology |
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1116 |
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Program Komputer dan Teknologi Informasi |
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- |
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DKI JAKARTA |
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IOS1116 |
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2019-05-04T00:26:53Z |
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2019-06-10T12:27:52Z |
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