Several Robust Techniques In Two-Groups Unbiased Linear Classification

The fundamental difficulty in classification problem is how to assign an observation accurately to the group it belongs. This thesis is written based on the limitations and weaknesses of the Fisher linear classification analysis and its robust version based on the minimum covariance determinant e...

Full description

Saved in:

Bibliographic Details
Main Author:	Okwonu, Friday Zinzendoff
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA1 Mathematics (General)
Online Access:	http://eprints.usm.my/43378/1/Friday%20Zinzendoff%20Okwonu24.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-usm-ep.43378
record_format	uketd_dc
spelling	my-usm-ep.433782019-04-12T05:26:10Z Several Robust Techniques In Two-Groups Unbiased Linear Classification 2013-10 Okwonu, Friday Zinzendoff QA1 Mathematics (General) The fundamental difficulty in classification problem is how to assign an observation accurately to the group it belongs. This thesis is written based on the limitations and weaknesses of the Fisher linear classification analysis and its robust version based on the minimum covariance determinant estimator. The Fisher’s procedure is not robust while the robust version depends upon information obtained from the half set. This study develops several techniques to address the weaknesses of the two methods. They are: M linear classification rule, filter linear classification rule, weighted linear classification rule and linear combination linear classification rule. These procedures are developed in such a way that the influential observations are modeled alongside the regular observations. The robustness and stability of these techniques depends on the separation parameters. Contamination models and control variables were used to investigate the classification performance of these linear classification rules. Classification difference was used to compare the classification performance of the proposed techniques over the Fisher linear classification analysis and the Fisher linear classification analysis based on the minimum covariance determinant procedures. The mean probability of correct classification for each procedure was used to compare the mean of the optimal probability of correct classification obtained from the uncontaminated data set in order to ascertain robustness, breakdown and admissibility of these techniques. 2013-10 Thesis http://eprints.usm.my/43378/ http://eprints.usm.my/43378/1/Friday%20Zinzendoff%20Okwonu24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1 Mathematics (General)
spellingShingle	QA1 Mathematics (General) Okwonu, Friday Zinzendoff Several Robust Techniques In Two-Groups Unbiased Linear Classification
description	The fundamental difficulty in classification problem is how to assign an observation accurately to the group it belongs. This thesis is written based on the limitations and weaknesses of the Fisher linear classification analysis and its robust version based on the minimum covariance determinant estimator. The Fisher’s procedure is not robust while the robust version depends upon information obtained from the half set. This study develops several techniques to address the weaknesses of the two methods. They are: M linear classification rule, filter linear classification rule, weighted linear classification rule and linear combination linear classification rule. These procedures are developed in such a way that the influential observations are modeled alongside the regular observations. The robustness and stability of these techniques depends on the separation parameters. Contamination models and control variables were used to investigate the classification performance of these linear classification rules. Classification difference was used to compare the classification performance of the proposed techniques over the Fisher linear classification analysis and the Fisher linear classification analysis based on the minimum covariance determinant procedures. The mean probability of correct classification for each procedure was used to compare the mean of the optimal probability of correct classification obtained from the uncontaminated data set in order to ascertain robustness, breakdown and admissibility of these techniques.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Okwonu, Friday Zinzendoff
author_facet	Okwonu, Friday Zinzendoff
author_sort	Okwonu, Friday Zinzendoff
title	Several Robust Techniques In Two-Groups Unbiased Linear Classification
title_short	Several Robust Techniques In Two-Groups Unbiased Linear Classification
title_full	Several Robust Techniques In Two-Groups Unbiased Linear Classification
title_fullStr	Several Robust Techniques In Two-Groups Unbiased Linear Classification
title_full_unstemmed	Several Robust Techniques In Two-Groups Unbiased Linear Classification
title_sort	several robust techniques in two-groups unbiased linear classification
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Matematik
publishDate	2013
url	http://eprints.usm.my/43378/1/Friday%20Zinzendoff%20Okwonu24.pdf
_version_	1747821203921305600

Several Robust Techniques In Two-Groups Unbiased Linear Classification

Similar Items