Predictive Framework for Imbalance Dataset

The purpose of this research is to seek and propose a new predictive maintenance framework which can be used to generate a prediction model for deterioration of process materials. Real yield data which was obtained from Fuji Electric Malaysia has been used in this research. The existing data pre-pro...

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主要作者:	Megat Norulazmi, Megat Mohamed Noor
格式:	Thesis
語言:	eng eng
出版:	2012
主題:	HA Statistics
在線閱讀:	https://etd.uum.edu.my/3603/1/s91447.pdf https://etd.uum.edu.my/3603/7/s91447.pdf
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id	my-uum-etd.3603
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Jusoh, Shaidah
topic	HA Statistics
spellingShingle	HA Statistics Megat Norulazmi, Megat Mohamed Noor Predictive Framework for Imbalance Dataset
description	The purpose of this research is to seek and propose a new predictive maintenance framework which can be used to generate a prediction model for deterioration of process materials. Real yield data which was obtained from Fuji Electric Malaysia has been used in this research. The existing data pre-processing and classification methodologies have been adapted in this research. Properties of the proposed framework include; developing an approach to correlate materials defects, developing an approach to represent data attributes features, analyzing various ratio and types of data re-sampling, analyzing the impact of data dimension reduction for various data size, and partitioning data size and algorithmic schemes against the prediction performance. Experimental results suggested that the class probability distribution function of a prediction model has to be closer to a training dataset; less skewed environment enable learning schemes to discover better function F in a bigger Fall space within a higher dimensional feature space, data sampling and partition size is appear to proportionally improve the precision and recall if class distribution ratios are balanced. A comparative study was also conducted and showed that the proposed approaches have performed better. This research was conducted based on limited number of datasets, test sets and variables. Thus, the obtained results are applicable only to the study domain with selected datasets. This research has introduced a new predictive maintenance framework which can be used in manufacturing industries to generate a prediction model based on the deterioration of process materials. Consequently, this may allow manufactures to conduct predictive maintenance not only for equipments but also process materials. The major contribution of this research is a step by step guideline which consists of methods/approaches in generating a prediction for process materials.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Megat Norulazmi, Megat Mohamed Noor
author_facet	Megat Norulazmi, Megat Mohamed Noor
author_sort	Megat Norulazmi, Megat Mohamed Noor
title	Predictive Framework for Imbalance Dataset
title_short	Predictive Framework for Imbalance Dataset
title_full	Predictive Framework for Imbalance Dataset
title_fullStr	Predictive Framework for Imbalance Dataset
title_full_unstemmed	Predictive Framework for Imbalance Dataset
title_sort	predictive framework for imbalance dataset
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2012
url	https://etd.uum.edu.my/3603/1/s91447.pdf https://etd.uum.edu.my/3603/7/s91447.pdf
_version_	1747827611150581760
spelling	my-uum-etd.36032019-11-13T00:03:05Z Predictive Framework for Imbalance Dataset 2012 Megat Norulazmi, Megat Mohamed Noor Jusoh, Shaidah Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences HA Statistics The purpose of this research is to seek and propose a new predictive maintenance framework which can be used to generate a prediction model for deterioration of process materials. Real yield data which was obtained from Fuji Electric Malaysia has been used in this research. The existing data pre-processing and classification methodologies have been adapted in this research. Properties of the proposed framework include; developing an approach to correlate materials defects, developing an approach to represent data attributes features, analyzing various ratio and types of data re-sampling, analyzing the impact of data dimension reduction for various data size, and partitioning data size and algorithmic schemes against the prediction performance. Experimental results suggested that the class probability distribution function of a prediction model has to be closer to a training dataset; less skewed environment enable learning schemes to discover better function F in a bigger Fall space within a higher dimensional feature space, data sampling and partition size is appear to proportionally improve the precision and recall if class distribution ratios are balanced. A comparative study was also conducted and showed that the proposed approaches have performed better. This research was conducted based on limited number of datasets, test sets and variables. Thus, the obtained results are applicable only to the study domain with selected datasets. This research has introduced a new predictive maintenance framework which can be used in manufacturing industries to generate a prediction model based on the deterioration of process materials. Consequently, this may allow manufactures to conduct predictive maintenance not only for equipments but also process materials. The major contribution of this research is a step by step guideline which consists of methods/approaches in generating a prediction for process materials. 2012 Thesis https://etd.uum.edu.my/3603/ https://etd.uum.edu.my/3603/1/s91447.pdf text eng validuser https://etd.uum.edu.my/3603/7/s91447.pdf text eng public http://sierra.uum.edu.my/record=b1250825~S1 Ph.D. doctoral Universiti Utara Malaysia Abe, H., Ohsaki, M., Yokoi, H., & Yamaguchi, T. (2006). New Frontiers in Artificial Intelligence. Springer Berlin/ Heidelberg. Alpaydin, E. (2010). Introduction to Machine Learning. 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