Predicting Accuracy of Income a Year Using Rough Set Theory

The main objective of the experiments is to predict the accuracy of Adult dataset whether the income exceeds $50K per year or below $50K. Specifically, the objectives are to determine the best discretization method, split factor, reduction method, classifier and to build the classification model. In...

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Main Author:	Zuraihah, Ngadengon
Format:	Thesis
Language:	eng eng
Published:	2009
Subjects:	QA273-280 Probabilities Mathematical statistics
Online Access:	https://etd.uum.edu.my/2066/1/Zuraihah_Ngadengon.pdf https://etd.uum.edu.my/2066/2/1.Zuraihah_Ngadengon.pdf
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id	my-uum-etd.2066
record_format	uketd_dc
spelling	my-uum-etd.20662013-07-24T12:14:14Z Predicting Accuracy of Income a Year Using Rough Set Theory 2009 Zuraihah, Ngadengon Jusoh, Shaidah College of Arts and Sciences (CAS) College of Arts and Sciences QA273-280 Probabilities. Mathematical statistics The main objective of the experiments is to predict the accuracy of Adult dataset whether the income exceeds $50K per year or below $50K. Specifically, the objectives are to determine the best discretization method, split factor, reduction method, classifier and to build the classification model. In the experiments, the prediction of accuracy of the Adult dataset is developed by using rough set theory and Rosetta software while Knowledge Data Discovery (KDD) is used as the methodology. The Adult dataset that had been used in the experiments is comprises of 48,842 instances but only 24,999 instances is used along the experiments. Then, the data was randomly split into training data and testing data by using nine splits factor, which are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9. The result obtained from the experiments showed that the best discretization method is Naive Algorithm, the best split factor is 0.6, the best reduction method is Johnson's Algorithm and the best classifier is Standard Voting. The highest percentage of accuracy achieved by the classification model developed using the rough set theory is 87.12%. The experiments showed that rough set theory is a useful approach to analyze the Adult dataset because the accuracy achieved in the experiments exceeds the previous methods that have been used before. 2009 Thesis https://etd.uum.edu.my/2066/ https://etd.uum.edu.my/2066/1/Zuraihah_Ngadengon.pdf application/pdf eng validuser https://etd.uum.edu.my/2066/2/1.Zuraihah_Ngadengon.pdf application/pdf eng public masters masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Jusoh, Shaidah
topic	QA273-280 Probabilities Mathematical statistics
spellingShingle	QA273-280 Probabilities Mathematical statistics Zuraihah, Ngadengon Predicting Accuracy of Income a Year Using Rough Set Theory
description	The main objective of the experiments is to predict the accuracy of Adult dataset whether the income exceeds $50K per year or below $50K. Specifically, the objectives are to determine the best discretization method, split factor, reduction method, classifier and to build the classification model. In the experiments, the prediction of accuracy of the Adult dataset is developed by using rough set theory and Rosetta software while Knowledge Data Discovery (KDD) is used as the methodology. The Adult dataset that had been used in the experiments is comprises of 48,842 instances but only 24,999 instances is used along the experiments. Then, the data was randomly split into training data and testing data by using nine splits factor, which are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9. The result obtained from the experiments showed that the best discretization method is Naive Algorithm, the best split factor is 0.6, the best reduction method is Johnson's Algorithm and the best classifier is Standard Voting. The highest percentage of accuracy achieved by the classification model developed using the rough set theory is 87.12%. The experiments showed that rough set theory is a useful approach to analyze the Adult dataset because the accuracy achieved in the experiments exceeds the previous methods that have been used before.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Zuraihah, Ngadengon
author_facet	Zuraihah, Ngadengon
author_sort	Zuraihah, Ngadengon
title	Predicting Accuracy of Income a Year Using Rough Set Theory
title_short	Predicting Accuracy of Income a Year Using Rough Set Theory
title_full	Predicting Accuracy of Income a Year Using Rough Set Theory
title_fullStr	Predicting Accuracy of Income a Year Using Rough Set Theory
title_full_unstemmed	Predicting Accuracy of Income a Year Using Rough Set Theory
title_sort	predicting accuracy of income a year using rough set theory
granting_institution	Universiti Utara Malaysia
granting_department	College of Arts and Sciences (CAS)
publishDate	2009
url	https://etd.uum.edu.my/2066/1/Zuraihah_Ngadengon.pdf https://etd.uum.edu.my/2066/2/1.Zuraihah_Ngadengon.pdf
_version_	1747827256497012736

Predicting Accuracy of Income a Year Using Rough Set Theory

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