Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data

Machine learning technologies have been growing rapidly in recent years. Researchers have come up with several data processing architectures, enabling machines to consume, interpret, and produce understandable output from real-world data to improve the quality of our lives. The NeuCube architecture...

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Main Author:	Abdullah, Mohd Hafizul Afifi
Format:	Thesis
Language:	English English English
Published:	2021
Subjects:	TJ212-225 Control engineering systems Automatic machinery (General)
Online Access:	http://eprints.uthm.edu.my/1064/1/24p%20MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH.pdf http://eprints.uthm.edu.my/1064/2/MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1064/3/MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH%20WATERMARK.pdf
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id	my-uthm-ep.1064
record_format	uketd_dc
spelling	my-uthm-ep.10642021-08-22T08:31:21Z Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data 2021-01 Abdullah, Mohd Hafizul Afifi TJ212-225 Control engineering systems. Automatic machinery (General) Machine learning technologies have been growing rapidly in recent years. Researchers have come up with several data processing architectures, enabling machines to consume, interpret, and produce understandable output from real-world data to improve the quality of our lives. The NeuCube architecture is a data processing architecture for spatio/spectro-temporal data which consists of four main modules: a spike encoding module, a recurrent SNN reservoir, an output module, and an optimization module. Despite it has been utilised on many various applications, most improvement of the architecture focuses on user experience rather than improving the result accuracy. Upon exploration of the architecture, the weighted k-nearest neighbours algorithm used for the classification module is found to be prone to misclassification as it relies solely on the majority voting rule to determine the class for new data vector. Additionally, it does not consider the class-specific fuzzy weight information during the classification process. Therefore, a data modelling mechanism which implements PIfwkNN classifier algorithm for improving the overall classification accuracy of the NeuCube architecture has been proposed. The proposed data modelling applies an additional class-specific fuzzy weight information to new data vectors during the classification process. In this research, the optimal parameters set for experiments has also been identified. The approach has been validated by using the Kuala Krai Rainfall Dataset, Dow Jones Index Data Set, and Gold Price and Performance Dataset for the 3-days earlier and 1-day earlier event prediction. From the experiments, the improved personalised data modelling using PIfwkNN classifier has shown a significant increase in terms of overall classification accuracy as compared to the conventional MLP, fkNN, and NeuCube with wkNN classifier. 2021-01 Thesis http://eprints.uthm.edu.my/1064/ http://eprints.uthm.edu.my/1064/1/24p%20MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH.pdf text en public http://eprints.uthm.edu.my/1064/2/MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/1064/3/MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TJ212-225 Control engineering systems Automatic machinery (General)
spellingShingle	TJ212-225 Control engineering systems Automatic machinery (General) Abdullah, Mohd Hafizul Afifi Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
description	Machine learning technologies have been growing rapidly in recent years. Researchers have come up with several data processing architectures, enabling machines to consume, interpret, and produce understandable output from real-world data to improve the quality of our lives. The NeuCube architecture is a data processing architecture for spatio/spectro-temporal data which consists of four main modules: a spike encoding module, a recurrent SNN reservoir, an output module, and an optimization module. Despite it has been utilised on many various applications, most improvement of the architecture focuses on user experience rather than improving the result accuracy. Upon exploration of the architecture, the weighted k-nearest neighbours algorithm used for the classification module is found to be prone to misclassification as it relies solely on the majority voting rule to determine the class for new data vector. Additionally, it does not consider the class-specific fuzzy weight information during the classification process. Therefore, a data modelling mechanism which implements PIfwkNN classifier algorithm for improving the overall classification accuracy of the NeuCube architecture has been proposed. The proposed data modelling applies an additional class-specific fuzzy weight information to new data vectors during the classification process. In this research, the optimal parameters set for experiments has also been identified. The approach has been validated by using the Kuala Krai Rainfall Dataset, Dow Jones Index Data Set, and Gold Price and Performance Dataset for the 3-days earlier and 1-day earlier event prediction. From the experiments, the improved personalised data modelling using PIfwkNN classifier has shown a significant increase in terms of overall classification accuracy as compared to the conventional MLP, fkNN, and NeuCube with wkNN classifier.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Abdullah, Mohd Hafizul Afifi
author_facet	Abdullah, Mohd Hafizul Afifi
author_sort	Abdullah, Mohd Hafizul Afifi
title	Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
title_short	Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
title_full	Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
title_fullStr	Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
title_full_unstemmed	Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
title_sort	improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2021
url	http://eprints.uthm.edu.my/1064/1/24p%20MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH.pdf http://eprints.uthm.edu.my/1064/2/MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1064/3/MOHD%20HAFIZUL%20AFIFI%20BIN%20ABDULLAH%20WATERMARK.pdf
_version_	1747830731942395904

Improved personalised data modelling using parameter independent fuzzy weighted k-nearest neighbour for spatio/spectro-temporal data

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