Abstract
This paper introduces an ensemble k-nearest neighbor with neuro-fuzzy method for the classification. A new paradigm for classification is proposed. The structure of the system includes the use of neural network, fuzzy logic and k-nearest neighbor. The first part is the beginning stages of learning by using 1-hidden layer neural network. In stage 2, the error from the first stage is forwarded to Mandani fuzzy system. The final step is the defuzzification process to create new dataset for classification. This new data is called "transformed training set". The parameters of the learning process are applied to the test dataset to create a "transformed testing set". Class of the transformed testing set is determined by using k-nearest neighbor. A variety of standard datasets from UCI were tested with our proposed. The fabulous classification results obtained from the experiments can confirm the good performance of ensemble k-nearest neighbor with neuro-fuzzy method.
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Saetern, K., Eiamkanitchat, N. (2014). An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification. In: Boonkrong, S., Unger, H., Meesad, P. (eds) Recent Advances in Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-319-06538-0_5
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DOI: https://doi.org/10.1007/978-3-319-06538-0_5
Publisher Name: Springer, Cham
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