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Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis

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@article{IJASEIT4986,
   author = {Ashraf Osman Ibrahim and Siti Mariyam Shamsuddin and Abdulrazak Yahya Saleh and Ali Ahmed and Mohd Arfian Ismail and Shahreen Kasim},
   title = {Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {2},
   year = {2019},
   pages = {609--615},
   keywords = {local search; breast cancer; neural network; NSGA-II; ANN.},
   abstract = {

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.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4986},    doi = {10.18517/ijaseit.9.2.4986} }

EndNote

%A Ibrahim, Ashraf Osman
%A Shamsuddin, Siti Mariyam
%A Saleh, Abdulrazak Yahya
%A Ahmed, Ali
%A Ismail, Mohd Arfian
%A Kasim, Shahreen
%D 2019
%T Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis
%B 2019
%9 local search; breast cancer; neural network; NSGA-II; ANN.
%! Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis
%K local search; breast cancer; neural network; NSGA-II; ANN.
%X 

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.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4986 %R doi:10.18517/ijaseit.9.2.4986 %J International Journal on Advanced Science, Engineering and Information Technology %V 9 %N 2 %@ 2088-5334

IEEE

Ashraf Osman Ibrahim,Siti Mariyam Shamsuddin,Abdulrazak Yahya Saleh,Ali Ahmed,Mohd Arfian Ismail and Shahreen Kasim,"Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 2, pp. 609-615, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.2.4986.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ibrahim, Ashraf Osman
AU  - Shamsuddin, Siti Mariyam
AU  - Saleh, Abdulrazak Yahya
AU  - Ahmed, Ali
AU  - Ismail, Mohd Arfian
AU  - Kasim, Shahreen
PY  - 2019
TI  - Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 2
Y2  - 2019
SP  - 609
EP  - 615
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - local search; breast cancer; neural network; NSGA-II; ANN.
N2  - 

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.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4986 DO - 10.18517/ijaseit.9.2.4986

RefWorks

RT Journal Article
ID 4986
A1 Ibrahim, Ashraf Osman
A1 Shamsuddin, Siti Mariyam
A1 Saleh, Abdulrazak Yahya
A1 Ahmed, Ali
A1 Ismail, Mohd Arfian
A1 Kasim, Shahreen
T1 Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 2
YR 2019
SP 609
OP 615
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 local search; breast cancer; neural network; NSGA-II; ANN.
AB 

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.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4986 DO - 10.18517/ijaseit.9.2.4986