Communications for Statistical Applications and Methods

  • 제14권2호
  • /
  • Pages.377-388
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  • 2007
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Comparison Study of Multi-class Classification Methods

  • Bae, Wha-Soo (Department of Data Science and Institute of Statistical Information, Inje University) ;
  • Jeon, Gab-Dong (Telluce Corporation) ;
  • Seok, Kyung-Ha (Department of Data Science and Institute of Statistical Information, Inje University)
  • 발행 : 2007.08.31
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초록

As one of multi-class classification methods, ECOC (Error Correcting Output Coding) method is known to have low classification error rate. This paper aims at suggesting effective multi-class classification method (1) by comparing various encoding methods and decoding methods in ECOC method and (2) by comparing ECOC method and direct classification method. Both SVM (Support Vector Machine) and logistic regression model were used as binary classifiers in comparison.

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참고문헌

  1. Aha, D. W. and Bankert, R. L. (1997). Cloud classification using error correcting output codes. Artificial Intelligence Applications: Natural Resources, Agriculture and Environmental Science, 11, 13-28
  2. Berger, A (1999). Error-correcting output coding for text classification. In Proceedings of International Joint Conference Artificial Intelligence, IJCAI'99, Stockholm, Sweden
  3. Dietterich, T. G. and Bakiri, G. (1991). Error-correcting output codes: A general method for improving multi-class inductive learning programs. In Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), 572-577, AAAI Press
  4. Dietterich, T. G. and Bakiri, G. (1995). Solving multi-class learning problems via errorcorrecting output codes. Journal of Artificial Intelligence Research, 2, 263-286
  5. Kittler, J., Ghaderi, R., Windeatt, T. and Matas, G. (2001). Face verification using error correcting output codes. In Computer Vision and Pattern Recognition CVPR01, Hawaii, IEEE Press
  6. Kuncheva, L. I. (2005). Using diversity measures for generating error -correcting output codes in classifier ensembles. Pattern Recognition Letters, 26, 83-90 https://doi.org/10.1016/j.patrec.2004.08.019
  7. Lee, Y., Lin Y. and Wahba, G. (2004). Multicategory support vector machines: theory and application to the classification of microarray data and satellite radiance data. Journal of the American Statistical Association, 99, 67-81 https://doi.org/10.1198/016214504000000098
  8. Schapire, R. E. (1997). Using output codes to boost multi-class learning problems. In 14th International Conference on Machine Learning, 313-321, Morgan Kaufman
  9. Windeatt, T. and Ghaderi, R. (2003). Coding and decoding strategies for multi-class learning problems. Information Fusion, 4, 11-21 https://doi.org/10.1016/S1566-2535(02)00101-X