An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification
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 b...
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th-cmuir.6653943832-390622015-06-16T08:01:25Z An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification Saetern,K. Eiamkanitchat,N. Control and Systems Engineering Computer Science (all) 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. © Springer International Publishing Switzerland 2014. 2015-06-16T08:01:25Z 2015-06-16T08:01:25Z 2014-01-01 Conference Paper 21945357 2-s2.0-84906886880 10.1007/978-3-319-06538-0_5 http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84906886880&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/39062 Springer Verlag |
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Control and Systems Engineering Computer Science (all) Saetern,K. Eiamkanitchat,N. An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification |
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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. © Springer International Publishing Switzerland 2014. |
format |
Conference or Workshop Item |
author |
Saetern,K. Eiamkanitchat,N. |
author_facet |
Saetern,K. Eiamkanitchat,N. |
author_sort |
Saetern,K. |
title |
An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification |
title_short |
An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification |
title_full |
An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification |
title_fullStr |
An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification |
title_full_unstemmed |
An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification |
title_sort |
ensemble k-nearest neighbor with neuro-fuzzy method for classification |
publisher |
Springer Verlag |
publishDate |
2015 |
url |
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84906886880&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/39062 |
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