한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

한국컴퓨터정보학회 (Korean Society of Computer Information)
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Design and Implementation of the Ensemble-based Classification Model by Using k-means Clustering

  • Song, Sung-Yeol (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Khil, A-Ra (Dept. of Computer Science and Engineering, Soongsil University)
  • 투고 : 2015.07.15
  • 심사 : 2015.09.22
  • 발행 : 2015.10.30
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초록

In this paper, we propose the ensemble-based classification model which extracts just new data patterns from the streaming-data by using clustering and generates new classification models to be added to the ensemble in order to reduce the number of data labeling while it keeps the accuracy of the existing system. The proposed technique performs clustering of similar patterned data from streaming data. It performs the data labeling to each cluster at the point when a certain amount of data has been gathered. The proposed technique applies the K-NN technique to the classification model unit in order to keep the accuracy of the existing system while it uses a small amount of data. The proposed technique is efficient as using about 3% less data comparing with the existing technique as shown the simulation results for benchmarks, thereby using clustering.

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참고문헌

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