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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...

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Main Author: Iqbal, Umer
Format: Thesis
Language:English
English
English
Published: 2018
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TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Online Access:http://eprints.uthm.edu.my/266/1/24p%20UMER%20IQBAL.pdf
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spelling my.uthm.eprints.2662021-07-21T02:08:14Z http://eprints.uthm.edu.my/266/ An improved functional link neural network for data classification 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 NonPeerReviewed text en http://eprints.uthm.edu.my/266/1/24p%20UMER%20IQBAL.pdf text en http://eprints.uthm.edu.my/266/2/UMER%20IQBAL%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/266/3/UMER%20IQBAL%20WATERMARK.pdf Iqbal, Umer (2018) An improved functional link neural network for data classification. Doctoral thesis, Universiti Tun Hussein Onn Malaysia.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
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
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
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
http://eprints.uthm.edu.my/266/
_version_ 1738580715566530560
score 13.211869

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