Attribute Set Weighting and Decomposition Approaches for Reduct Computation

This research is mainly in the Rough Set theory based knowledge reduction for data classification within the data mining framework. To facilitate the Rough Set based classification, two main knowledge reduction models are proposed. The first model is an approximate approach for object reducts compu...

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Main Author:	Al-Radaideh, Qasem Ahmad
Format:	Thesis
Language:	English
Published:	2005
Subjects:	Decomposition method Rough sets Data mining
Online Access:	http://psasir.upm.edu.my/id/eprint/5853/1/FSKTM_2005_7%20IR.pdf
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spelling	my-upm-ir.58532022-01-06T07:11:47Z Attribute Set Weighting and Decomposition Approaches for Reduct Computation 2005-07 Al-Radaideh, Qasem Ahmad This research is mainly in the Rough Set theory based knowledge reduction for data classification within the data mining framework. To facilitate the Rough Set based classification, two main knowledge reduction models are proposed. The first model is an approximate approach for object reducts computation used particularly for the data classification purposes. This approach emphasizes on assigning weights for each attribute in the attributes set. The weights give indication for the importance of an attribute to be considered in the reduct. This proposed approach is named Object Reduct by Attribute Weighting (ORAW). A variation of this approach is proposed to compute full reduct and named Full Reduct by Attribute Weighting (FRAW).The second proposed approach deals with large datasets particularly with large number of attributes. This approach utilizes the principle of incremental attribute set decomposition to generate an approximate reduct to represent the entire dataset. This proposed approach is termed for Reduct by Attribute Set Decomposition (RASD).The proposed reduct computation approaches are extensively experimented and evaluated. The evaluation is mainly in two folds: first is to evaluate the proposed approaches as Rough Set based methods where the classification accuracy is used as an evaluation measure. The well known IO-fold cross validation method is used to estimate the classification accuracy. The second fold is to evaluate the approaches as knowledge reduction methods where the size of the reduct is used as a reduction measure. The approaches are compared to other reduct computation methods and to other none Rough Set based classification methods. The proposed approaches are applied to various standard domains datasets from the UCI repository. The results of the experiments showed a very good performance for the proposed approaches as classification methods and as knowledge reduction methods. The accuracy of the ORAW approach outperformed the Johnson approach over all the datasets. It also produces better accuracy over the Exhaustive and the Standard Integer Programming (SIP) approaches for the majority of the datasets used in the experiments. For the RASD approach, it is compared to other classification methods and it shows very competitive results in term of classification accuracy and reducts size. As a conclusion, the proposed approaches have shown competitive and even better accuracy in most tested domains. The experiment results indicate that the proposed approaches as Rough classifiers give good performance across different classification problems and they can be promising methods in solving classification problems. Moreover, the experiments proved that the incremental vertical decomposition framework is an appealing method for knowledge reduction over large datasets within the framework of Rough Set based classification. Decomposition method Rough sets Data mining 2005-07 Thesis http://psasir.upm.edu.my/id/eprint/5853/ http://psasir.upm.edu.my/id/eprint/5853/1/FSKTM_2005_7%20IR.pdf text en public doctoral Universiti Putra Malaysia Decomposition method Rough sets Data mining Computer Science and Information Technology Sulaiman, Md. Nasir
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Sulaiman, Md. Nasir
topic	Decomposition method Rough sets Data mining
spellingShingle	Decomposition method Rough sets Data mining Al-Radaideh, Qasem Ahmad Attribute Set Weighting and Decomposition Approaches for Reduct Computation
description	This research is mainly in the Rough Set theory based knowledge reduction for data classification within the data mining framework. To facilitate the Rough Set based classification, two main knowledge reduction models are proposed. The first model is an approximate approach for object reducts computation used particularly for the data classification purposes. This approach emphasizes on assigning weights for each attribute in the attributes set. The weights give indication for the importance of an attribute to be considered in the reduct. This proposed approach is named Object Reduct by Attribute Weighting (ORAW). A variation of this approach is proposed to compute full reduct and named Full Reduct by Attribute Weighting (FRAW).The second proposed approach deals with large datasets particularly with large number of attributes. This approach utilizes the principle of incremental attribute set decomposition to generate an approximate reduct to represent the entire dataset. This proposed approach is termed for Reduct by Attribute Set Decomposition (RASD).The proposed reduct computation approaches are extensively experimented and evaluated. The evaluation is mainly in two folds: first is to evaluate the proposed approaches as Rough Set based methods where the classification accuracy is used as an evaluation measure. The well known IO-fold cross validation method is used to estimate the classification accuracy. The second fold is to evaluate the approaches as knowledge reduction methods where the size of the reduct is used as a reduction measure. The approaches are compared to other reduct computation methods and to other none Rough Set based classification methods. The proposed approaches are applied to various standard domains datasets from the UCI repository. The results of the experiments showed a very good performance for the proposed approaches as classification methods and as knowledge reduction methods. The accuracy of the ORAW approach outperformed the Johnson approach over all the datasets. It also produces better accuracy over the Exhaustive and the Standard Integer Programming (SIP) approaches for the majority of the datasets used in the experiments. For the RASD approach, it is compared to other classification methods and it shows very competitive results in term of classification accuracy and reducts size. As a conclusion, the proposed approaches have shown competitive and even better accuracy in most tested domains. The experiment results indicate that the proposed approaches as Rough classifiers give good performance across different classification problems and they can be promising methods in solving classification problems. Moreover, the experiments proved that the incremental vertical decomposition framework is an appealing method for knowledge reduction over large datasets within the framework of Rough Set based classification.
format	Thesis
qualification_level	Doctorate
author	Al-Radaideh, Qasem Ahmad
author_facet	Al-Radaideh, Qasem Ahmad
author_sort	Al-Radaideh, Qasem Ahmad
title	Attribute Set Weighting and Decomposition Approaches for Reduct Computation
title_short	Attribute Set Weighting and Decomposition Approaches for Reduct Computation
title_full	Attribute Set Weighting and Decomposition Approaches for Reduct Computation
title_fullStr	Attribute Set Weighting and Decomposition Approaches for Reduct Computation
title_full_unstemmed	Attribute Set Weighting and Decomposition Approaches for Reduct Computation
title_sort	attribute set weighting and decomposition approaches for reduct computation
granting_institution	Universiti Putra Malaysia
granting_department	Computer Science and Information Technology
publishDate	2005
url	http://psasir.upm.edu.my/id/eprint/5853/1/FSKTM_2005_7%20IR.pdf
_version_	1747810494996021248

Attribute Set Weighting and Decomposition Approaches for Reduct Computation

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