An approach to improve functional link neural network training using modified artificial bee colony for classification task
Classification is one of the most frequent studies in the area of Artificial Neural Network (ANNs). One of the best known types of ANNs is the Multilayer Perceptron (MLP). However, MLP usually requires a rather large amount of available measures in order to achieve good classification accuracy. To o...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2013
|
| Online Access: | http://journalarticle.ukm.my/6647/ http://journalarticle.ukm.my/6647/1/4347-10130-1-PB.pdf |
| _version_ | 1848811223920934912 |
|---|---|
| author | Yana Mazwin Mohmad Hassim, Rozaida Ghazali, |
| author_facet | Yana Mazwin Mohmad Hassim, Rozaida Ghazali, |
| author_sort | Yana Mazwin Mohmad Hassim, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Classification is one of the most frequent studies in the area of Artificial Neural Network (ANNs). One of the best known types of ANNs is the Multilayer Perceptron (MLP). However, MLP usually requires a rather large amount of available measures in order to achieve good classification accuracy. To overcome this, a Functional Link Neural Networks (FLNN), which has single layer of trainable connection weight is used. The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. In this paper, a modified Artificial Bee Colony (mABC) is used to recover the BP drawbacks. With modifications on the employed bee’s exploitation phase, the implementation of the mABC as a learning scheme for FLNN has given a better accuracy result for the classification tasks. |
| first_indexed | 2025-11-14T23:43:00Z |
| format | Article |
| id | oai:generic.eprints.org:6647 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T23:43:00Z |
| publishDate | 2013 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:66472016-12-14T06:41:48Z http://journalarticle.ukm.my/6647/ An approach to improve functional link neural network training using modified artificial bee colony for classification task Yana Mazwin Mohmad Hassim, Rozaida Ghazali, Classification is one of the most frequent studies in the area of Artificial Neural Network (ANNs). One of the best known types of ANNs is the Multilayer Perceptron (MLP). However, MLP usually requires a rather large amount of available measures in order to achieve good classification accuracy. To overcome this, a Functional Link Neural Networks (FLNN), which has single layer of trainable connection weight is used. The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence especially for solving non-linearly separable classification problems. In this paper, a modified Artificial Bee Colony (mABC) is used to recover the BP drawbacks. With modifications on the employed bee’s exploitation phase, the implementation of the mABC as a learning scheme for FLNN has given a better accuracy result for the classification tasks. Penerbit Universiti Kebangsaan Malaysia 2013-12 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/6647/1/4347-10130-1-PB.pdf Yana Mazwin Mohmad Hassim, and Rozaida Ghazali, (2013) An approach to improve functional link neural network training using modified artificial bee colony for classification task. Asia-Pacific Journal of Information Technology and Multimedia, 2 (2). pp. 63-71. ISSN 2289-2192 http://ejournals.ukm.my/apjitm/index |
| spellingShingle | Yana Mazwin Mohmad Hassim, Rozaida Ghazali, An approach to improve functional link neural network training using modified artificial bee colony for classification task |
| title | An approach to improve functional link neural network training using modified artificial bee colony for classification task |
| title_full | An approach to improve functional link neural network training using modified artificial bee colony for classification task |
| title_fullStr | An approach to improve functional link neural network training using modified artificial bee colony for classification task |
| title_full_unstemmed | An approach to improve functional link neural network training using modified artificial bee colony for classification task |
| title_short | An approach to improve functional link neural network training using modified artificial bee colony for classification task |
| title_sort | approach to improve functional link neural network training using modified artificial bee colony for classification task |
| url | http://journalarticle.ukm.my/6647/ http://journalarticle.ukm.my/6647/ http://journalarticle.ukm.my/6647/1/4347-10130-1-PB.pdf |