Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

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

DOI QR Code

Pattern Classification Model Design and Performance Comparison for Data Mining of Time Series Data

시계열 자료의 데이터마이닝을 위한 패턴분류 모델설계 및 성능비교

  • 이수용 (연세대학교 인문예술대학 교양교직과) ;
  • 이경중 (연세대학교 보건과학대학 의공학부)
  • Received : 2011.11.03
  • Accepted : 2011.12.03
  • Published : 2011.12.25
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

본 연구는 순차적인 시계열 자료들에서 가장 최근의 추세가 반영될 수 있는 패턴분류 모델을 설계하였다. 의사결정을 지원하는 데이터마이닝 패턴분류 모델을 설계할 때 통계 기법과 인공지능 기법을 융합한 모델들이 기존의 모델보다 우수함을 입증하였다. 특히 퍼지이론과 융합된 패턴분류 모델들의 적중률이 상대적으로 더 향상되었다. 예를 들어, 통계적 이론을 기반으로 한 SVM모델과 퍼지소속함수와의 결합, 혹은 신경망과 FCM을 결합한 모델들의 성능이 우수하였다. 실험에서 사용한 패턴분류 모델들은 BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, Regression Analysis 등이다. 그리고 데이터베이스는 시계열 속성을 지닌 금융시장의 경제지표 DB(한국, KOSPI200 데이터베이스)와 병원 응급실의 부정맥환자에 대한 심전도 DB(미국 MIT-BIH 데이터베이스)들을 사용하였다.

Keywords

References

  1. V.X. Afonso and W. J Tompkins, "Detection Ventricular Fibrillation," IEEE Engineering in Medicine and Biology Magazine, vol. 14, no. 2, pp.152-159, 1995. https://doi.org/10.1109/51.376752
  2. H. Bae, S. Kim, H Kim, and B.W. Kwang, "A Comparative Study on the Prediction of KOSPI 200 Using Intelligent Approaches," International J ournal of Fuzzy Logic and Intelligent Systems, vol. 3, no. 1, pp.7-12, June, 2003. https://doi.org/10.5391/IJFIS.2003.3.1.007
  3. Y.G. Jang, S.J. Jang, S.O. Hwang, and Y.R. Yoon, "Prediction of the Successful Defibrillation using Hilbert-Huang Transform," Journal of The Institute of Electronics Engineers of Korea - SC, vol. 44, no. 5, pp. 45-54, 2007.
  4. D.S. Kim, "Stock and News Application of Intelligent Agent System," International J ournal of Fuzzy Logic and Intelligent Systems, vol. 3, no. 2, pp. 239-243, December, 2003. https://doi.org/10.5391/IJFIS.2003.3.2.239
  5. M.S. Kim and S.Y. Lee, "Pattern Classification for Biomedical Signal using BP Algorithm and SVM," J ournal of Fuzzy Logic and Intelligent Systems, vol. 14, no.1, pp. 82-87, 2004. https://doi.org/10.5391/JKIIS.2004.14.1.082
  6. C.F. Lin, "Fuzzy Support Vector Machine," IEEE Transactions on Neural Networks, vol. 13, no, 2, March, 2002.
  7. S.Y. 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
  8. S.Y. Lee, "On the Fuzzy Membership Function of fuzzy Support Vector Machines for Pattern Classification of Time Series Data," Journal of Fuzzy Logic and Intelligent Systems, vol. 17, no. 6, pp. 799-803, 2007. https://doi.org/10.5391/JKIIS.2007.17.6.799
  9. S.Y.Lee, A Comparative Study of Model Design and Performance Test in Financial Index Pattern Classification System, Doctoral Thesis, Yonsei University, 2004.
  10. S.Y.Lee, D.Y.Ahn, M.H.Song, and K.J.Lee, "The Classification of Electrocardiograph Arrythmia Pattern using Fuzzy Support Vector Machines", International Jounal of Fuzzy Logic and Intelligen Systems, Vol.11, no.3, pp.204-21-. 2011. https://doi.org/10.5391/IJFIS.2011.11.3.204
  11. S.Y.Lee, D.Y.Ahn, M.H.Song, and K.J.Lee, "The Arrythmia Pattern Classifier of Electrocardiograph using the Fuzzy SVM", 12th ISIS, Proceeding of ISIS2011, September , pp.526-529. 2011.
  12. J. Mill , A. Inoue, "An application of fuzzy support vectors, Fuzzy Information Processing Society," NAFIPS. 22nd International Conference of the North American, Chicargo, IL, pp.302-306, 2003.
  13. K.I. Minami, H.Nakajima, "Real-Time Discrimination of Ventricular Tachyarrhythmia with Fourier-Transform Neural Network," IEEE Trans. Biomedical Engineering, vol. 46, no. 2, pp. 179-185, 1999. https://doi.org/10.1109/10.740880
  14. M.I. Oiws, A.H. Abou-Zied and A.M. Youssef, "Study of features based on Nonlinear Dynamical Modeling in ECG Arrhythmia Detection and Classification," IEEE Transaction Biomedical Engineering, vol. 49, no.7, pp.733-736, July, 2002. https://doi.org/10.1109/TBME.2002.1010858
  15. L.Y. Shyu, Y.H. Wu, and W. Hu, "Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG," IEEE Transactions on Biomedicine and Engineering, vol. 51, no. 7, pp.1269-1273, 2004. https://doi.org/10.1109/TBME.2004.824131
  16. M.H. Song, J. Lee, S.P. Cho, K.J. Lee, and S.K. Yoo, "Support Vector Machine Based Arrhythmia Classification Using Reduced Features," International Journal of Control, Automation, and Systems, vol. 3, no, 4, pp. 571-579, 2005.
  17. M.H. Song, J. Lee, S.P. Cho, and K.J. Lee, "SVM Classifier for the Detection of Ventricular Fibrillation," Journal of The Institute of Electronics Engineers of Korea - SC, vol. 42, no. 5, 2005.
  18. E. Spyrou, and others, Fuzzy support vector machines for image classification fusing MPEG-7 visual descriptors, 2nd European Workshop, Integration of Knowledge, Semantics and Digital Media Technology, 2005-Dec, pp.23-30, 2005.
  19. T. Sugiura, H. Hirata, Y. Harada and T. Kazui, "Automatic Discrimination of Arrhythmia waveforms using Fuzzy Logic," in Proc. Of IEEE Conf. on Engineering in Medicine and Biology Society, vol. 20, no.1, pp. 108-111, Hong Kong, China, October-November, 1998.
  20. Y. Wang, S. Wang, and K.K. La, "A New Fuzzy Support Vector Machine to Evaluate Credit Risk," IEEE Transactions on Fuzzy Systems, vol. 13, no, 6, pp. 820-831, 2005. https://doi.org/10.1109/TFUZZ.2005.859320

Cited by

  1. Implementation of Hospital Examination Reservation System Using Data Mining Technique vol.21, pp.2, 2015, https://doi.org/10.4258/hir.2015.21.2.95