Integrated smoothed location model and data reduction approaches for multi variables classification

Smoothed Location Model is a classification rule that deals with mixture of continuous variables and binary variables simultaneously. This rule discriminates groups in a parametric form using conditional distribution of the continuous variables given each pattern of the binary variables. To conduct...

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Main Author:	Hashibah, Hamid
Format:	Thesis
Language:	eng eng
Published:	2014
Subjects:	QA71-90 Instruments and machines
Online Access:	https://etd.uum.edu.my/4420/1/s92365.pdf https://etd.uum.edu.my/4420/2/s92365_abstract.pdf
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id	my-uum-etd.4420
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Mahat, Nor Idayu
topic	QA71-90 Instruments and machines
spellingShingle	QA71-90 Instruments and machines Hashibah, Hamid Integrated smoothed location model and data reduction approaches for multi variables classification
description	Smoothed Location Model is a classification rule that deals with mixture of continuous variables and binary variables simultaneously. This rule discriminates groups in a parametric form using conditional distribution of the continuous variables given each pattern of the binary variables. To conduct a practical classification analysis, the objects must first be sorted into the cells of a multinomial table generated from the binary variables. Then, the parameters in each cell will be estimated using the sorted objects. However, in many situations, the estimated parameters are poor if the number of binary is large relative to the size of sample. Large binary variables will create too many multinomial cells which are empty, leading to high sparsity problem and finally give exceedingly poor performance for the constructed rule. In the worst case scenario, the rule cannot be constructed. To overcome such shortcomings, this study proposes new strategies to extract adequate variables that contribute to optimum performance of the rule. Combinations of two extraction techniques are introduced, namely 2PCA and PCA+MCA with new cutpoints of eigenvalue and total variance explained, to determine adequate extracted variables which lead to minimum misclassification rate. The outcomes from these extraction techniques are used to construct the smoothed location models, which then produce two new approaches of classification called 2PCALM and 2DLM. Numerical evidence from simulation studies demonstrates that the computed misclassification rate indicates no significant difference between the extraction techniques in normal and non-normal data. Nevertheless, both proposed approaches are slightly affected for non-normal data and severely affected for highly overlapping groups. Investigations on some real data sets show that the two approaches are competitive with, and better than other existing classification methods. The overall findings reveal that both proposed approaches can be considered as improvement to the location model, and alternatives to other classification methods particularly in handling mixed variables with large binary size.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Hashibah, Hamid
author_facet	Hashibah, Hamid
author_sort	Hashibah, Hamid
title	Integrated smoothed location model and data reduction approaches for multi variables classification
title_short	Integrated smoothed location model and data reduction approaches for multi variables classification
title_full	Integrated smoothed location model and data reduction approaches for multi variables classification
title_fullStr	Integrated smoothed location model and data reduction approaches for multi variables classification
title_full_unstemmed	Integrated smoothed location model and data reduction approaches for multi variables classification
title_sort	integrated smoothed location model and data reduction approaches for multi variables classification
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2014
url	https://etd.uum.edu.my/4420/1/s92365.pdf https://etd.uum.edu.my/4420/2/s92365_abstract.pdf
_version_	1747827735063953408
spelling	my-uum-etd.44202022-07-27T01:24:25Z Integrated smoothed location model and data reduction approaches for multi variables classification 2014 Hashibah, Hamid Mahat, Nor Idayu Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA71-90 Instruments and machines Smoothed Location Model is a classification rule that deals with mixture of continuous variables and binary variables simultaneously. This rule discriminates groups in a parametric form using conditional distribution of the continuous variables given each pattern of the binary variables. To conduct a practical classification analysis, the objects must first be sorted into the cells of a multinomial table generated from the binary variables. Then, the parameters in each cell will be estimated using the sorted objects. However, in many situations, the estimated parameters are poor if the number of binary is large relative to the size of sample. Large binary variables will create too many multinomial cells which are empty, leading to high sparsity problem and finally give exceedingly poor performance for the constructed rule. In the worst case scenario, the rule cannot be constructed. To overcome such shortcomings, this study proposes new strategies to extract adequate variables that contribute to optimum performance of the rule. Combinations of two extraction techniques are introduced, namely 2PCA and PCA+MCA with new cutpoints of eigenvalue and total variance explained, to determine adequate extracted variables which lead to minimum misclassification rate. The outcomes from these extraction techniques are used to construct the smoothed location models, which then produce two new approaches of classification called 2PCALM and 2DLM. Numerical evidence from simulation studies demonstrates that the computed misclassification rate indicates no significant difference between the extraction techniques in normal and non-normal data. Nevertheless, both proposed approaches are slightly affected for non-normal data and severely affected for highly overlapping groups. Investigations on some real data sets show that the two approaches are competitive with, and better than other existing classification methods. The overall findings reveal that both proposed approaches can be considered as improvement to the location model, and alternatives to other classification methods particularly in handling mixed variables with large binary size. 2014 Thesis https://etd.uum.edu.my/4420/ https://etd.uum.edu.my/4420/1/s92365.pdf text eng public https://etd.uum.edu.my/4420/2/s92365_abstract.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Abdi, H. & Valentin, D. (2007). Multiple Correspondence Analysis. In N. 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