Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/54377
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dc.contributor.authorJirawat Teyakomeen_US
dc.contributor.authorNarissara Eiamkanitchaten_US
dc.date.accessioned2018-09-04T10:12:37Z-
dc.date.available2018-09-04T10:12:37Z-
dc.date.issued2015-01-01en_US
dc.identifier.other2-s2.0-84966545425en_US
dc.identifier.other10.1109/ICITEED.2015.7409021en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84966545425&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/54377-
dc.description.abstract© 2015 IEEE. This paper presents the improved algorithm for the Hybrid Approach of Neural network and Level-2 Fuzzy set (HANN-L2F). The main structure is including 2 parts. The first part is Neuro-Fuzzy system, including the MLP Neural network with the combination of the level-2 Fuzzy system. The second part is using k-nearest neighbor to classify the output from Neuro-fuzzy. The HANN-L2F is an algorithm with high classification performance. However, the process to determine the number of membership functions in HANN-L2F is take time and sometimes results the high number of clusters, this make the high complexity of the overall process. This paper, including the data adjustment in HANN-L2F by using the determination process and implement k-means algorithm to find the appropriate number of clusters. Several types of standard datasets from UCI repository machine learning are used to verify the performance of the propose algorithm. The additional data adjustment process can improve the classification performance of HANN-L2F, furthermore, can reduce the classification time as displayed in the experimental results.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.titleImprovements the HANN-L2F for classification by using k-meansen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleProceedings - 2015 7th International Conference on Information Technology and Electrical Engineering: Envisioning the Trend of Computer, Information and Engineering, ICITEE 2015en_US
article.stream.affiliationsChiang Mai Universityen_US
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