Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models
One of the main difficulties in real-world data classification and analysis tasks is that the data distribution can be imbalanced. In this paper, a variant of the supervised learning neural network from the Adaptive Resonance Theory (ART) family, i.e., Fuzzy ARTMAP (FAM) which is equipped with a con...
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| Format: | Book Section |
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IEEE
2011
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| _version_ | 1796856520041824256 |
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| author | Tan, S. C. Watada, J. Ibrahim, Z. Khalid, Marzuki Jau, L. W. Chew, L. C. |
| author_facet | Tan, S. C. Watada, J. Ibrahim, Z. Khalid, Marzuki Jau, L. W. Chew, L. C. |
| author_sort | Tan, S. C. |
| collection | ePrints |
| description | One of the main difficulties in real-world data classification and analysis tasks is that the data distribution can be imbalanced. In this paper, a variant of the supervised learning neural network from the Adaptive Resonance Theory (ART) family, i.e., Fuzzy ARTMAP (FAM) which is equipped with a conflict-resolving facility, is proposed to classify an imbalanced dataset that represents a real problem in the semiconductor industry. The FAM model is combined with the Dynamic Decay Adjustment (DDA) algorithm to form a hybrid FAMDDA network. The classification results of FAM and FAMDDA are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed FAMDDA network in undertaking classification problems with imbalanced datasets. |
| first_indexed | 2024-03-05T18:44:11Z |
| format | Book Section |
| id | utm.eprints-29253 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:44:11Z |
| publishDate | 2011 |
| publisher | IEEE |
| record_format | dspace |
| spelling | utm.eprints-292532017-02-05T00:07:06Z http://eprints.utm.my/29253/ Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models Tan, S. C. Watada, J. Ibrahim, Z. Khalid, Marzuki Jau, L. W. Chew, L. C. QA75 Electronic computers. Computer science One of the main difficulties in real-world data classification and analysis tasks is that the data distribution can be imbalanced. In this paper, a variant of the supervised learning neural network from the Adaptive Resonance Theory (ART) family, i.e., Fuzzy ARTMAP (FAM) which is equipped with a conflict-resolving facility, is proposed to classify an imbalanced dataset that represents a real problem in the semiconductor industry. The FAM model is combined with the Dynamic Decay Adjustment (DDA) algorithm to form a hybrid FAMDDA network. The classification results of FAM and FAMDDA are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed FAMDDA network in undertaking classification problems with imbalanced datasets. IEEE 2011 Book Section PeerReviewed Tan, S. C. and Watada, J. and Ibrahim, Z. and Khalid, Marzuki and Jau, L. W. and Chew, L. C. (2011) Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, pp. 1084-1089. ISBN 978-142447317-5 http://dx.doi.org/10.1109/FUZZY.2011.6007330 10.1109/FUZZY.2011.6007330 |
| spellingShingle | QA75 Electronic computers. Computer science Tan, S. C. Watada, J. Ibrahim, Z. Khalid, Marzuki Jau, L. W. Chew, L. C. Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models |
| title | Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models |
| title_full | Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models |
| title_fullStr | Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models |
| title_full_unstemmed | Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models |
| title_short | Learning with imbalanced datasets using fuzzy ARTMAP-based neural network models |
| title_sort | learning with imbalanced datasets using fuzzy artmap based neural network models |
| topic | QA75 Electronic computers. Computer science |
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