Fuzzy inference system model from non-fuzzy clustering output
Fuzzy Inference System (FIS) is a process of mapping input into the desired output using fuzzy logic theory where decisions can be made or patterns are discerned. This study aims to discuss on how non-fuzzy clustering output can be used to construct a model of FIS. Here, the proposed idea is to show...
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| Format: | Article |
| Language: | English |
| Published: |
Medwell Publications
2019
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| Online Access: | http://eprints.uthm.edu.my/4076/1/AJ%202019%20%28215%29.pdf |
| _version_ | 1825709776719314944 |
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| author | Hamzah, Nur Atiqah Kek, Sie Long |
| author_facet | Hamzah, Nur Atiqah Kek, Sie Long |
| author_sort | Hamzah, Nur Atiqah |
| collection | UTHM |
| description | Fuzzy Inference System (FIS) is a process of mapping input into the desired output using fuzzy logic theory where decisions can be made or patterns are discerned. This study aims to discuss on how non-fuzzy clustering output can be used to construct a model of FIS. Here, the proposed idea is to show the efficient use of the FIS as a prediction model for the data classification. In this study, employment income, self-employment income, property and transfer received are taken into account for clustering the household income data. Then, the FIS prediction model is built using the center values of clusters formed and the output of FIS is compared to the original cluster in which the best fit prediction model to the data is determined. In conclusion, the best prediction model in identifying income class is discovered based on the Root Mean Square Error (RMSE) value computed. |
| first_indexed | 2024-03-05T21:47:33Z |
| format | Article |
| id | uthm.eprints-4076 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:47:33Z |
| publishDate | 2019 |
| publisher | Medwell Publications |
| record_format | dspace |
| spelling | uthm.eprints-40762021-11-24T04:49:35Z http://eprints.uthm.edu.my/4076/ Fuzzy inference system model from non-fuzzy clustering output Hamzah, Nur Atiqah Kek, Sie Long QA76 Computer software Fuzzy Inference System (FIS) is a process of mapping input into the desired output using fuzzy logic theory where decisions can be made or patterns are discerned. This study aims to discuss on how non-fuzzy clustering output can be used to construct a model of FIS. Here, the proposed idea is to show the efficient use of the FIS as a prediction model for the data classification. In this study, employment income, self-employment income, property and transfer received are taken into account for clustering the household income data. Then, the FIS prediction model is built using the center values of clusters formed and the output of FIS is compared to the original cluster in which the best fit prediction model to the data is determined. In conclusion, the best prediction model in identifying income class is discovered based on the Root Mean Square Error (RMSE) value computed. Medwell Publications 2019 Article PeerReviewed text en http://eprints.uthm.edu.my/4076/1/AJ%202019%20%28215%29.pdf Hamzah, Nur Atiqah and Kek, Sie Long (2019) Fuzzy inference system model from non-fuzzy clustering output. Journal of Engineering and Applied Sciences, 14 (12). pp. 4035-4042. ISSN 1816-949x https://dx.doi.org/ 10.36478/jeasci.2019.4035.4042 |
| spellingShingle | QA76 Computer software Hamzah, Nur Atiqah Kek, Sie Long Fuzzy inference system model from non-fuzzy clustering output |
| title | Fuzzy inference system model from non-fuzzy clustering output |
| title_full | Fuzzy inference system model from non-fuzzy clustering output |
| title_fullStr | Fuzzy inference system model from non-fuzzy clustering output |
| title_full_unstemmed | Fuzzy inference system model from non-fuzzy clustering output |
| title_short | Fuzzy inference system model from non-fuzzy clustering output |
| title_sort | fuzzy inference system model from non fuzzy clustering output |
| topic | QA76 Computer software |
| url | http://eprints.uthm.edu.my/4076/1/AJ%202019%20%28215%29.pdf |
| work_keys_str_mv | AT hamzahnuratiqah fuzzyinferencesystemmodelfromnonfuzzyclusteringoutput AT keksielong fuzzyinferencesystemmodelfromnonfuzzyclusteringoutput |