Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification
Hybrid technique for Self Organizing Map and Particle Swarm Optimization approach is commonly implemented in clustering area. In this paper, a hybrid approach that is based on Enhanced Self Organizing Map and Particle Swarm Optimization (ESOM/PSO) for classification is proposed. Enhanced Self Organi...
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2009
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my.utm.151522020-07-20T01:24:09Z http://eprints.utm.my/id/eprint/15152/ Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification Hasan, Shafaatunnur Shamsuddin, Siti Mariyam Yusob, Bariah QA75 Electronic computers. Computer science Hybrid technique for Self Organizing Map and Particle Swarm Optimization approach is commonly implemented in clustering area. In this paper, a hybrid approach that is based on Enhanced Self Organizing Map and Particle Swarm Optimization (ESOM/PSO) for classification is proposed. Enhanced Self Organization map which based on Kohonen network structure is to improve the quality of the data classification and labeling. New formulation of hexagonal lattice area is used for the enhancement Self Organizing Map structure. The proposed hybrid ESOM/PSO algorithm uses PSO to evolve the weights for ESOM. The weights are trained by ESOM in the first stage. In the second stage, they are optimized by PSO. 2009 Conference or Workshop Item PeerReviewed Hasan, Shafaatunnur and Shamsuddin, Siti Mariyam and Yusob, Bariah (2009) Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification. In: Konferensi Nasional Teknologi Informasi dan Aplikasinya, 2009, Palembang, Indonesia. |
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QA75 Electronic computers. Computer science |
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QA75 Electronic computers. Computer science Hasan, Shafaatunnur Shamsuddin, Siti Mariyam Yusob, Bariah Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification |
| description |
Hybrid technique for Self Organizing Map and Particle Swarm Optimization approach is commonly implemented in clustering area. In this paper, a hybrid approach that is based on Enhanced Self Organizing Map and Particle Swarm Optimization (ESOM/PSO) for classification is proposed. Enhanced Self Organization map which based on Kohonen network structure is to improve the quality of the data classification and labeling. New formulation of hexagonal lattice area is used for the enhancement Self Organizing Map structure. The proposed hybrid ESOM/PSO algorithm uses PSO to evolve the weights for ESOM. The weights are trained by ESOM in the first stage. In the second stage, they are optimized by PSO. |
| format |
Conference or Workshop Item |
| author |
Hasan, Shafaatunnur Shamsuddin, Siti Mariyam Yusob, Bariah |
| author_facet |
Hasan, Shafaatunnur Shamsuddin, Siti Mariyam Yusob, Bariah |
| author_sort |
Hasan, Shafaatunnur |
| title |
Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification |
| title_short |
Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification |
| title_full |
Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification |
| title_fullStr |
Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification |
| title_full_unstemmed |
Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification |
| title_sort |
enhanced self organizing map (esom) and particle swarm optimization (pso) for classification |
| publishDate |
2009 |
| url |
http://eprints.utm.my/id/eprint/15152/ |
| _version_ |
1674066113795719168 |
| score |
13.251813 |