Cascade Generalization and Complementary Neural Networks for Multiclass Classification
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Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85138978266
Journal Title
International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022
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SCOPUS
Bibliographic Citation
International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 (2022)
Suggested Citation
Nilnumpetch C., Amornsamankul S., Kraipeerapun P. Cascade Generalization and Complementary Neural Networks for Multiclass Classification. International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 (2022). doi:10.1109/ICECET55527.2022.9873449 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84363
Title
Cascade Generalization and Complementary Neural Networks for Multiclass Classification
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Abstract
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.