Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm

Existing classification techniques, which are previously proposed for eliminating data inconsistency, could not achieve an efficient parameter reduction in soft set theory as it affects the obtained decisions. Additionally, data decomposition based on previous algorithms could not achieve better par...

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Main Authors:	Mohammed, Mohammed Adam Taheir, Sadiq, Ali Safa, Ruzaini, Abdullah Arshah, Ernawan, Ferda, Mirjalili, Seyedali
Format:	Article
Language:	English
Published:	UTeM 2017
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/18619/ http://umpir.ump.edu.my/id/eprint/18619/ http://umpir.ump.edu.my/id/eprint/18619/1/2608-7035-1-SM.pdf

id	ump-18619
recordtype	eprints
spelling	ump-186192018-03-07T00:55:20Z http://umpir.ump.edu.my/id/eprint/18619/ Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm Mohammed, Mohammed Adam Taheir Sadiq, Ali Safa Ruzaini, Abdullah Arshah Ernawan, Ferda Mirjalili, Seyedali QA75 Electronic computers. Computer science QA76 Computer software Existing classification techniques, which are previously proposed for eliminating data inconsistency, could not achieve an efficient parameter reduction in soft set theory as it affects the obtained decisions. Additionally, data decomposition based on previous algorithms could not achieve better parameter reduction with available domain space. Meanwhile, the computational cost made during the combination generation of datasets can cause machine infinite state as Nondeterministic Polynomial time (NP). Although the decomposition scenario in the previous algorithms detects the reduction, it could not obtain the optimal decision. The contributions of this study are mainly focused on minimizing choices costs through adjusting the original classifications by decision partition order and enhancing the probability of search domain by a developed HPC algorithm. The results show that the decision partition order technique performs better in parameter reduction up to 50%, while other algorithms could not obtain any reduction in some scenarios. UTeM 2017-09 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/18619/1/2608-7035-1-SM.pdf Mohammed, Mohammed Adam Taheir and Sadiq, Ali Safa and Ruzaini, Abdullah Arshah and Ernawan, Ferda and Mirjalili, Seyedali (2017) Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm. Journal of Telecommunication, Electronic and Computer Engineering, 9 (2-7). pp. 143-148. ISSN 2289-8131 http://journal.utem.edu.my/index.php/jtec/article/view/2608
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Mohammed, Mohammed Adam Taheir Sadiq, Ali Safa Ruzaini, Abdullah Arshah Ernawan, Ferda Mirjalili, Seyedali Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm
description	Existing classification techniques, which are previously proposed for eliminating data inconsistency, could not achieve an efficient parameter reduction in soft set theory as it affects the obtained decisions. Additionally, data decomposition based on previous algorithms could not achieve better parameter reduction with available domain space. Meanwhile, the computational cost made during the combination generation of datasets can cause machine infinite state as Nondeterministic Polynomial time (NP). Although the decomposition scenario in the previous algorithms detects the reduction, it could not obtain the optimal decision. The contributions of this study are mainly focused on minimizing choices costs through adjusting the original classifications by decision partition order and enhancing the probability of search domain by a developed HPC algorithm. The results show that the decision partition order technique performs better in parameter reduction up to 50%, while other algorithms could not obtain any reduction in some scenarios.
format	Article
author	Mohammed, Mohammed Adam Taheir Sadiq, Ali Safa Ruzaini, Abdullah Arshah Ernawan, Ferda Mirjalili, Seyedali
author_facet	Mohammed, Mohammed Adam Taheir Sadiq, Ali Safa Ruzaini, Abdullah Arshah Ernawan, Ferda Mirjalili, Seyedali
author_sort	Mohammed, Mohammed Adam Taheir
title	Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm
title_short	Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm
title_full	Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm
title_fullStr	Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm
title_full_unstemmed	Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm
title_sort	soft set decision/forecasting system based on hybrid parameter reduction algorithm
publisher	UTeM
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18619/ http://umpir.ump.edu.my/id/eprint/18619/ http://umpir.ump.edu.my/id/eprint/18619/1/2608-7035-1-SM.pdf
first_indexed	2023-09-18T22:26:29Z
last_indexed	2023-09-18T22:26:29Z
_version_	1777416000409436160

Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm

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