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Mahalanobis Taguchi System을 이용한 다변량 시스템의 해석에 관한 연구

Analysis of Multivariate System Using Mahalanobis Taguchi System

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초록

Mahalanobis Taguchi System (MTS) is a pattern information technology, which has been used in different diagnostic applications to make quantitative decisions by constructing a multivariate measurement scale using data analytic methods without any assumption regarding statistical distribution. The MTS performs Taguchi’s fractional factorial design based on the Mahahlanobis Distance (MS) as a performance metric. In this work, MTS is used for analyzing Wisconsin Breast Cancer data which has ten attributes. Ten different tests are conducted for the data to determine if the patient has cancer or not. Also, MTS is used for reducing the number of test to define the relationship between each attribute and diagnosis result. The accuracy of diagnosis is compare with two different previous research.

Abstract

Mahalanobis Taguchi System (MTS) is a pattern information technology, which has been used in different diagnostic applications to make quantitative decisions by constructing a multivariate measurement scale using data analytic methods without any assumption regarding statistical distribution. The MTS performs Taguchi’s fractional factorial design based on the Mahahlanobis Distance (MS) as a performance metric. In this work, MTS is used for analyzing Wisconsin Breast Cancer data which has ten attributes. Ten different tests are conducted for the data to determine if the patient has cancer or not. Also, MTS is used for reducing the number of test to define the relationship between each attribute and diagnosis result. The accuracy of diagnosis is compare with two different previous research.

참고문헌 (10)

  1. Taguchi, G., "New Trends in Multivariate Diagnosis", Indian Journal of Statistics, 62(2), pp.233~248 (2000)
  2. Jain, Anil K., "Statistical Pattern Recognition:A Review", IEEE Transaction on Pattern Analysis and Machine Intelligence, 22(1) (2000)
  3. Taguchi, S., "Mahalanobis Taguchi System", ASI Taguchi Symposium (2000)
  4. "Mahalanobis Distance:A Theoretical and Practical Approach"[URL]
  5. Hayashi, S., "A New Manufacturing Control System using Mahalanobis Distance for Maximizing Productivity", IEEE Transactions, pp.59~62 (2001)
  6. Asada, M., "Wafer Yield Prediction by the Mahalanobis-Taguchi System", IIE Transactions, pp.25~28 (2001)
  7. Wu, Y., "Pattern Recognition using Mahalanobis Distance", TPD Symposium, pp.1~14 (1996)
  8. Taguchi G., "The Mahalanobis Taguchi System" (2001), McGraw Hill Press
  9. Wolberg, W. H., "Multisurface method od pattern seperation for medical diagnosis applied to breast cytology", Porceeding of the national academy of Science, pp.9193~9196 (1990)
  10. Zhang, J., "Selecting typical instance in instance-based learning", Proceeding of ninth international machine learning conference, pp.470~479 (1992)

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