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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
UTeM
2017
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/18619/1/2608-7035-1-SM.pdf |
| _version_ | 1796992300819152896 |
|---|---|
| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T12:18:02Z |
| format | Article |
| id | UMPir18619 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:18:02Z |
| publishDate | 2017 |
| publisher | UTeM |
| record_format | dspace |
| spelling | UMPir186192018-03-07T00:55:20Z 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. (Published) http://journal.utem.edu.my/index.php/jtec/article/view/2608 |
| 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 |
| title | 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_short | Soft Set Decision/Forecasting System Based on Hybrid Parameter Reduction Algorithm |
| title_sort | soft set decision forecasting system based on hybrid parameter reduction algorithm |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/18619/1/2608-7035-1-SM.pdf |
| work_keys_str_mv | AT mohammedmohammedadamtaheir softsetdecisionforecastingsystembasedonhybridparameterreductionalgorithm AT sadiqalisafa softsetdecisionforecastingsystembasedonhybridparameterreductionalgorithm AT ruzainiabdullaharshah softsetdecisionforecastingsystembasedonhybridparameterreductionalgorithm AT ernawanferda softsetdecisionforecastingsystembasedonhybridparameterreductionalgorithm AT mirjaliliseyedali softsetdecisionforecastingsystembasedonhybridparameterreductionalgorithm |