Weighted kernel regression in solving small sample problems for regression framework and its variants

In real engineering problems, regression plays an important role as a tool to approximate an unknown target function. One of the primary regression problems is the insufficient number of training samples during regression of a model to find the relationship between input and output of samples. These...

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Main Author:	Shapiai @ Abd. Razak, Mohd. Ibrahim
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/77743/1/MohdIbrahimShapiaiFKE2014.pdf
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id	my-utm-ep.77743
record_format	uketd_dc
spelling	my-utm-ep.777432018-07-04T11:42:51Z Weighted kernel regression in solving small sample problems for regression framework and its variants 2014-01 Shapiai @ Abd. Razak, Mohd. Ibrahim TK Electrical engineering. Electronics Nuclear engineering In real engineering problems, regression plays an important role as a tool to approximate an unknown target function. One of the primary regression problems is the insufficient number of training samples during regression of a model to find the relationship between input and output of samples. These samples do not provide enough information which leads to difficulty in the regression. Current techniques focusing on the selection of appropriate model parameters use several data-driven techniques and none of these techniques offer generalized and better solutions to the problems. Incorporation of prior knowledge is a plausible technique to improve the quality of the regression for data-driven techniques but this technique is based on preselection of coefficients in solving the problem, thus ignoring the consequence of the selection. To improve regression quality for small training samples problems, this thesis explored a new technique known as Weighted Kernel Regression (WKR) based on the Nadaraya-Watson Kernel Regression (NWKR) when solving small training samples problems. Besides WKR, the study introduced the incorporation of prior knowledge based on Pareto-optimality concept that utilizes the genetic algorithmbased multi-objective optimization technique to compute the Pareto optimal solutions. Instead of relying on pre-selection of coefficients to incorporate the knowledge, the proposed technique uses post-selection of solutions as it is less subjective, especially when used for solving small sample problems. The experimental results of the proposed technique using several benchmark problems were evaluated and compared with existing techniques. The findings showed that the proposed technique not only can offer better regression accuracy but also flexibility in generalizing the solution, especially when the available training samples are insufficient. 2014-01 Thesis http://eprints.utm.my/id/eprint/77743/ http://eprints.utm.my/id/eprint/77743/1/MohdIbrahimShapiaiFKE2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:90559 phd doctoral Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Shapiai @ Abd. Razak, Mohd. Ibrahim Weighted kernel regression in solving small sample problems for regression framework and its variants
description	In real engineering problems, regression plays an important role as a tool to approximate an unknown target function. One of the primary regression problems is the insufficient number of training samples during regression of a model to find the relationship between input and output of samples. These samples do not provide enough information which leads to difficulty in the regression. Current techniques focusing on the selection of appropriate model parameters use several data-driven techniques and none of these techniques offer generalized and better solutions to the problems. Incorporation of prior knowledge is a plausible technique to improve the quality of the regression for data-driven techniques but this technique is based on preselection of coefficients in solving the problem, thus ignoring the consequence of the selection. To improve regression quality for small training samples problems, this thesis explored a new technique known as Weighted Kernel Regression (WKR) based on the Nadaraya-Watson Kernel Regression (NWKR) when solving small training samples problems. Besides WKR, the study introduced the incorporation of prior knowledge based on Pareto-optimality concept that utilizes the genetic algorithmbased multi-objective optimization technique to compute the Pareto optimal solutions. Instead of relying on pre-selection of coefficients to incorporate the knowledge, the proposed technique uses post-selection of solutions as it is less subjective, especially when used for solving small sample problems. The experimental results of the proposed technique using several benchmark problems were evaluated and compared with existing techniques. The findings showed that the proposed technique not only can offer better regression accuracy but also flexibility in generalizing the solution, especially when the available training samples are insufficient.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Shapiai @ Abd. Razak, Mohd. Ibrahim
author_facet	Shapiai @ Abd. Razak, Mohd. Ibrahim
author_sort	Shapiai @ Abd. Razak, Mohd. Ibrahim
title	Weighted kernel regression in solving small sample problems for regression framework and its variants
title_short	Weighted kernel regression in solving small sample problems for regression framework and its variants
title_full	Weighted kernel regression in solving small sample problems for regression framework and its variants
title_fullStr	Weighted kernel regression in solving small sample problems for regression framework and its variants
title_full_unstemmed	Weighted kernel regression in solving small sample problems for regression framework and its variants
title_sort	weighted kernel regression in solving small sample problems for regression framework and its variants
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	http://eprints.utm.my/id/eprint/77743/1/MohdIbrahimShapiaiFKE2014.pdf
_version_	1747817820079521792

Weighted kernel regression in solving small sample problems for regression framework and its variants

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