Cascade Generalization and Complementary Neural Networks for Multiclass Classification
This paper presents a technique for solving multiclass classification problems. Two existing techniques are combined which are cascade generalization and complementary neural networks. The unification of these two techniques can increase the efficiency of classification. Three small datasets from UC...
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th-mahidol.843632023-06-19T00:03:22Z Cascade Generalization and Complementary Neural Networks for Multiclass Classification Nilnumpetch C. Mahidol University Computer Science This paper presents a technique for solving multiclass classification problems. Two existing techniques are combined which are cascade generalization and complementary neural networks. The unification of these two techniques can increase the efficiency of classification. Three small datasets from UCI machine learning repository are tested in the experiment. These datasets are wireless indoor localization, user knowledge modeling, and alcohol QCM sensor. The proposed approach gives the average accuracy of 98.5%, 95.0%, and 96.4%, respectively, which are better than using individual techniques such as feedforward backpropagation neural network, complementary neural networks, and cascade generalization. 2023-06-18T17:03:22Z 2023-06-18T17:03:22Z 2022-01-01 Conference Paper International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 (2022) 10.1109/ICECET55527.2022.9873449 2-s2.0-85138978266 https://repository.li.mahidol.ac.th/handle/123456789/84363 SCOPUS |
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Computer Science Nilnumpetch C. Cascade Generalization and Complementary Neural Networks for Multiclass Classification |
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This paper presents a technique for solving multiclass classification problems. Two existing techniques are combined which are cascade generalization and complementary neural networks. The unification of these two techniques can increase the efficiency of classification. Three small datasets from UCI machine learning repository are tested in the experiment. These datasets are wireless indoor localization, user knowledge modeling, and alcohol QCM sensor. The proposed approach gives the average accuracy of 98.5%, 95.0%, and 96.4%, respectively, which are better than using individual techniques such as feedforward backpropagation neural network, complementary neural networks, and cascade generalization. |
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Mahidol University |
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Mahidol University Nilnumpetch C. |
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Conference or Workshop Item |
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Nilnumpetch C. |
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Nilnumpetch C. |
| title |
Cascade Generalization and Complementary Neural Networks for Multiclass Classification |
| title_short |
Cascade Generalization and Complementary Neural Networks for Multiclass Classification |
| title_full |
Cascade Generalization and Complementary Neural Networks for Multiclass Classification |
| title_fullStr |
Cascade Generalization and Complementary Neural Networks for Multiclass Classification |
| title_full_unstemmed |
Cascade Generalization and Complementary Neural Networks for Multiclass Classification |
| title_sort |
cascade generalization and complementary neural networks for multiclass classification |
| publishDate |
2023 |
| url |
https://repository.li.mahidol.ac.th/handle/123456789/84363 |
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1781413879904993280 |