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...

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Main Authors:	Yana Mazwin Mohmad Hassim, Rozaida Ghazali
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2013
Online Access:	http://journalarticle.ukm.my/6647/ http://journalarticle.ukm.my/6647/ http://journalarticle.ukm.my/6647/1/4347-10130-1-PB.pdf

id	ukm-6647
recordtype	eprints
spelling	ukm-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
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
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.
format	Article
author	Yana Mazwin Mohmad Hassim, Rozaida Ghazali,
spellingShingle	Yana Mazwin Mohmad Hassim, Rozaida Ghazali, An approach to improve functional link neural network training using modified artificial bee colony for classification task
author_facet	Yana Mazwin Mohmad Hassim, Rozaida Ghazali,
author_sort	Yana Mazwin Mohmad Hassim,
title	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_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_sort	approach to improve functional link neural network training using modified artificial bee colony for classification task
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2013
url	http://journalarticle.ukm.my/6647/ http://journalarticle.ukm.my/6647/ http://journalarticle.ukm.my/6647/1/4347-10130-1-PB.pdf
first_indexed	2023-09-18T19:47:30Z
last_indexed	2023-09-18T19:47:30Z
_version_	1777405998584037376

An approach to improve functional link neural network training using modified artificial bee colony for classification task

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