An improved functional link neural network for data classification

The goal of classification is to assign the pre-specified group or class to an instance based on the observed features related to that instance. The implementation of several classification models is challenging as some only work well when the underlying assumptions are satisfied. In order to genera...

全面介绍

Saved in:

书目详细资料
主要作者:	Iqbal, Umer
格式:	Thesis
语言:	English English English
出版:	2018
主题:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
在线阅读:	http://eprints.uthm.edu.my/266/1/24p%20UMER%20IQBAL.pdf http://eprints.uthm.edu.my/266/2/UMER%20IQBAL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/266/3/UMER%20IQBAL%20WATERMARK.pdf
标签:	添加标签没有标签, 成为第一个标记此记录!

id	my-uthm-ep.266
record_format	uketd_dc
spelling	my-uthm-ep.2662021-07-21T02:08:14Z An improved functional link neural network for data classification 2018-03 Iqbal, Umer TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television The goal of classification is to assign the pre-specified group or class to an instance based on the observed features related to that instance. The implementation of several classification models is challenging as some only work well when the underlying assumptions are satisfied. In order to generate the complex mapping between input and output space to build the arbitrary complex non-linear decision boundaries, neural networks has become prominent tool with wide range of applications. The recent techniques such as Multilayer Perceptron (MLP), standard Functional Link Neural Network (FLNN) and Chebyshev Functional Link Neural Network (CFLNN) outperformed their existing regression, multiple regression, quadratic regression, stepwise polynomials, K-nearest neighbor (K-NN), Naïve Bayesian classifier and logistic regression. This research work explores the insufficiencies of well- known CFLNN model where CFLNN utilizes functional expansion with large number of degree and coefficient value for inputs enhancement which increase computational complexity of the network. Accordingly, two alternative models namely; Genocchi Functional Link Neural Network (GFLNN) and Chebyshev Wavelets Functional Link Neural Network (CWFLNN) are proposed. The novelty of these approaches is that, GFLNN presents the functional expansions with less degree and small coefficient values to make less computational inputs for training to overcome the drawbacks of CFLNN. Whereas, CWFLNN is capable to generate more number of small coefficient value based basis functions with same degree of polynomials as compared to other polynomials and it has orthonormality condition therefore it has more accurate constant of functional expansion and can approximate the functions within the interval. These properties of CWFLNN are used to overcome the deficiencies of GFLNN. The significance of proposed models is verified by using statistical tests such as Freidman test based on accuracy ranking and pairwise comparison test. Moreover, MLP, standard FLNN and CFLNN are used for comparison. For experiments, benched marked data sets from UCI repository, SVMLIB data set and KEEL data sets are utilized. The CWFLNN reveals significant improvement (due to its generating more numbers of basis function property) in terms of classification accuracy and reduces the computational work. 2018-03 Thesis http://eprints.uthm.edu.my/266/ http://eprints.uthm.edu.my/266/1/24p%20UMER%20IQBAL.pdf text en public http://eprints.uthm.edu.my/266/2/UMER%20IQBAL%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/266/3/UMER%20IQBAL%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Iqbal, Umer An improved functional link neural network for data classification
description	The goal of classification is to assign the pre-specified group or class to an instance based on the observed features related to that instance. The implementation of several classification models is challenging as some only work well when the underlying assumptions are satisfied. In order to generate the complex mapping between input and output space to build the arbitrary complex non-linear decision boundaries, neural networks has become prominent tool with wide range of applications. The recent techniques such as Multilayer Perceptron (MLP), standard Functional Link Neural Network (FLNN) and Chebyshev Functional Link Neural Network (CFLNN) outperformed their existing regression, multiple regression, quadratic regression, stepwise polynomials, K-nearest neighbor (K-NN), Naïve Bayesian classifier and logistic regression. This research work explores the insufficiencies of well- known CFLNN model where CFLNN utilizes functional expansion with large number of degree and coefficient value for inputs enhancement which increase computational complexity of the network. Accordingly, two alternative models namely; Genocchi Functional Link Neural Network (GFLNN) and Chebyshev Wavelets Functional Link Neural Network (CWFLNN) are proposed. The novelty of these approaches is that, GFLNN presents the functional expansions with less degree and small coefficient values to make less computational inputs for training to overcome the drawbacks of CFLNN. Whereas, CWFLNN is capable to generate more number of small coefficient value based basis functions with same degree of polynomials as compared to other polynomials and it has orthonormality condition therefore it has more accurate constant of functional expansion and can approximate the functions within the interval. These properties of CWFLNN are used to overcome the deficiencies of GFLNN. The significance of proposed models is verified by using statistical tests such as Freidman test based on accuracy ranking and pairwise comparison test. Moreover, MLP, standard FLNN and CFLNN are used for comparison. For experiments, benched marked data sets from UCI repository, SVMLIB data set and KEEL data sets are utilized. The CWFLNN reveals significant improvement (due to its generating more numbers of basis function property) in terms of classification accuracy and reduces the computational work.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Iqbal, Umer
author_facet	Iqbal, Umer
author_sort	Iqbal, Umer
title	An improved functional link neural network for data classification
title_short	An improved functional link neural network for data classification
title_full	An improved functional link neural network for data classification
title_fullStr	An improved functional link neural network for data classification
title_full_unstemmed	An improved functional link neural network for data classification
title_sort	improved functional link neural network for data classification
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2018
url	http://eprints.uthm.edu.my/266/1/24p%20UMER%20IQBAL.pdf http://eprints.uthm.edu.my/266/2/UMER%20IQBAL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/266/3/UMER%20IQBAL%20WATERMARK.pdf
_version_	1747830568549089280

An improved functional link neural network for data classification

相似书籍