A hybrid-based modified adaptive fuzzy inference engine for pattern classification
The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a hybrid Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/69010/1/A%20hybrid-based%20modified%20adaptive%20fuzzy%20inference%20engine%20for%20pattern%20classification.pdf |
| _version_ | 1796978850394013696 |
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| author | Sayeed, Md. Shohel Ramli, Abdul Rahman Hossen, Md. Jakir Samsudin, Khairulmizam Rokhani, Fakhrul Zaman |
| author_facet | Sayeed, Md. Shohel Ramli, Abdul Rahman Hossen, Md. Jakir Samsudin, Khairulmizam Rokhani, Fakhrul Zaman |
| author_sort | Sayeed, Md. Shohel |
| collection | UPM |
| description | The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a hybrid Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data set. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the hybrid fuzzy clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed hybrid MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher's Iris data set and shown to be very competitive. |
| first_indexed | 2024-03-06T10:00:26Z |
| format | Conference or Workshop Item |
| id | upm.eprints-69010 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:00:26Z |
| publishDate | 2011 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-690102019-06-12T07:32:58Z http://psasir.upm.edu.my/id/eprint/69010/ A hybrid-based modified adaptive fuzzy inference engine for pattern classification Sayeed, Md. Shohel Ramli, Abdul Rahman Hossen, Md. Jakir Samsudin, Khairulmizam Rokhani, Fakhrul Zaman The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a hybrid Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data set. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the hybrid fuzzy clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed hybrid MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher's Iris data set and shown to be very competitive. IEEE 2011 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/69010/1/A%20hybrid-based%20modified%20adaptive%20fuzzy%20inference%20engine%20for%20pattern%20classification.pdf Sayeed, Md. Shohel and Ramli, Abdul Rahman and Hossen, Md. Jakir and Samsudin, Khairulmizam and Rokhani, Fakhrul Zaman (2011) A hybrid-based modified adaptive fuzzy inference engine for pattern classification. In: 2011 11th International Conference on Hybrid Intelligent Systems (HIS), 5-8 Dec. 2011, Melaka, Malaysia. (pp. 295-300). 10.1109/HIS.2011.6122121 |
| spellingShingle | Sayeed, Md. Shohel Ramli, Abdul Rahman Hossen, Md. Jakir Samsudin, Khairulmizam Rokhani, Fakhrul Zaman A hybrid-based modified adaptive fuzzy inference engine for pattern classification |
| title | A hybrid-based modified adaptive fuzzy inference engine for pattern classification |
| title_full | A hybrid-based modified adaptive fuzzy inference engine for pattern classification |
| title_fullStr | A hybrid-based modified adaptive fuzzy inference engine for pattern classification |
| title_full_unstemmed | A hybrid-based modified adaptive fuzzy inference engine for pattern classification |
| title_short | A hybrid-based modified adaptive fuzzy inference engine for pattern classification |
| title_sort | hybrid based modified adaptive fuzzy inference engine for pattern classification |
| url | http://psasir.upm.edu.my/id/eprint/69010/1/A%20hybrid-based%20modified%20adaptive%20fuzzy%20inference%20engine%20for%20pattern%20classification.pdf |
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