Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

On the Fuzzy Membership Function of Fuzzy Support Vector Machines for Pattern Classification of Time Series Data

퍼지서포트벡터기계의 시계열자료 패턴분류를 위한 퍼지소속 함수에 관한 연구

  • 이수용 (연세대학교 과학기술대학 컴퓨터정보통신공학부)
  • Published : 2007.12.25
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a new fuzzy membership function for FSVM(Fuzzy Support Vector Machines). We apply a fuzzy membership to each input point of SVM and reformulate SVM into fuzzy SVM (FSVM) such that different input points can make different contributions to the learning of decision surface. The proposed method enhances the SVM in reducing the effect of outliers and noises in data points. This paper compares classification and estimated performance of SVM, FSVM(1), and FSVM(2) model that are getting into the spotlight in time series prediction.

본 논문에서는 FSVM(Putty Support Vector Machine)의 퍼지소속함수를 새롭게 제안한다. SVM의 완화변수(slack-variable)에 퍼지소속함수를 결합하는 FSVM은 주어진 데이터베이스의 특성이 반영되어 안정적으로 분류성능을 향상시킬 수 있는 퍼지소속 함수를 필요로 한다. 시계열 자료의 패턴분류 성능을 비교하기 위하여 SVM, FSVM(1), 그리고 제안하는 FSVM(2) 등의 분류모델들을 비교 실험하였다. 사용한 데이터베이스는 한국금융시장의 시계열 경제지표 지수들이다.

Keywords

References

  1. S. Abe, Fuzzy LP-SVMs for Multiclass Problems, ESANN'2004 proceedings, pp. 429-434, Bruges (Belgium), 28-30 April 2004
  2. N. Cristianini, An Introduction to Support Vector Machines, Cambridge University Press, 2000
  3. T. Inoue, S. Abe, Fuzzy support vector machines for pattern classification, Neural Networks Proceedings. Vol. 2, pp. 1449-1454, 2001
  4. S.Y.Lee, S.Y.Sohn, C.E.Kim, Y.B.Lee, Fuzzy Support Vector Machine for Pattern Classification of Time Series Data of KOSPI200 Index, Journal of Fuzzy Logic and Intelligent Systems, Vol. 14, No.1, pp.52-56, 2004 https://doi.org/10.5391/JKIIS.2004.14.1.052
  5. S.Y.Lee, A Comparative Study of Model Design and Performance Test in Financial Index Pattern Classification System, Doctoral Thesis, Yonsei University, 2004
  6. C. F. Lin, 'Fuzzy Support Vector Machines', IEEE Transactions on Neural Networks, Vol.13, No.2, March 2002
  7. Y. Mao, Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection, Journal of Biomedicine and Biotechnology, Vol. 2, pp.160-171, 2005
  8. J. Mill, A. Inoue, An application of fuzzy support vectors, Fuzzy Information Processing Society, NAFIP5. 22nd International Conference of the North American, pp.302- 306, 2003
  9. X. Shi, H.D. Cheng, and L. Hu, Mass Detection and Classification in Breast Ultrasound Images Using Fuzzy SVM, Proceeding of ICIS, 2006
  10. E. Spyrou, G. Stamou, Y. vrithis, S. Kollias, Fuzzy support vector machines for image classification fusing MPEG-7 visual descriptors, 2nd European Workshop, Integration of Knowledge, Semantics and Digital Media Technology, 2005
  11. F. E. H. Tay, 'Application of support vector machines in financial time series forecasting', Omega 29, pp. 309-317, 2002 https://doi.org/10.1016/S0305-0483(01)00026-3
  12. V. N. Vapnik, Statistical Learning Theory, Wiley -Interscience Pub., 1998
  13. Y. Wang, S. Wang, and K. K. La, A New Fuzzy Support Vector Machine to Evaluate Credit Ris, IEEE Transactions on Fuzzy Systems, Vol. 13, No.6, pp. 820-831, 2005 https://doi.org/10.1109/TFUZZ.2005.859320
  14. H. Xia, B. Q. Hu, Feature selection using fuzzy support vector machines, Fuzzy Optimal Decis Making, vol. 5, pp.187 - 192, 2006 https://doi.org/10.1007/s10700-006-7336-8

Cited by

  1. The Classification of Electrocardiograph Arrhythmia Patterns using Fuzzy Support Vector Machines vol.11, pp.3, 2011, https://doi.org/10.5391/IJFIS.2011.11.3.204
  2. Pattern Classification Model Design and Performance Comparison for Data Mining of Time Series Data vol.21, pp.6, 2011, https://doi.org/10.5391/JKIIS.2011.21.6.730
  3. On Fuzzy Methods to Classify Quality Attributes in Kano Model vol.26, pp.6, 2016, https://doi.org/10.5391/JKIIS.2016.26.6.439