Journal of the Institute of Electronics Engineers of Korea CI (전자공학회논문지CI)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Relational Discriminant Analysis Using Prototype Reduction Schemes and Mahalanobis Distances

Prototype Reduction Schemes와 Mahalanobis 거리를 이용한 Relational Discriminant Analysis

  • Kim Sang-Woon (Dept. of Computer Science & Engineering, Myongji University)
  • 김상운 (명지대학교 컴퓨터공학과)
  • Published : 2006.01.01
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Abstract

RDA(Relational Discriminant Analysis) is a way of finding classifiers based on the dissimilarity measures among the prototypes extracted from feature vectors instead of the feature vectors themselves. Therefore, the accuracy of the RDA classifier is dependent on the methods of selecting prototypes and measuring proximities. In this paper we propose to utilize PRS(Prototype Reduction Schemes) and Mahalanobis distances to devise a method of increasing classification accuracies. Our experimental results demonstrate that the proposed mechanism increases the classification accuracy compared with the conventional approaches for samples involving real-life data sets as well as artificial data sets.

RDA(Relational Discriminant Analysis)는 패턴의 특징벡터 대신에 학습 패턴을 대표하는 프로토타입들과의 비유사도 벡터에 기반하여 식별기를 설계하는 방법이다. 따라서 RDA 식별기의 성능은 프로토타입을 선택하는 방법과 비유사도를 측정하는 방법에 따라 결정된다. 본 논문에서는 PRS(Prototype Reduction Schemes)를 이용하여 프로토타입을 추출한 다음, 샘플 벡터들간의 마할라노비스 거리에 의한 상관행렬로 RDA의 식별성능을 향상시키는 방법을 제안한다. 인공 데이터 및 실-생활 데이터를 대상으로 실험한 결과, 제안한 방법의 식별성능이 기존의 방법에 비하여 개선되었음을 확인하였다.

Keywords

References

  1. K Fukunaga, Introduction to Statistical Pattern Recognition, Second Edition, Academic Press, San Diego, 1990
  2. J. Ye, R. Janardan, c. H. Park and H. Park, 'An optimization criterion for generalized discriminant analysis on undersampled problems', IEEE Trans. Pattern Anal. and Machine Intell., vol. PAMI-26, no. 8, pp. 982 - 994, Aug. 2004 https://doi.org/10.1109/TPAMI.2004.37
  3. R. P. W. Duin, E. Pekalska and D. de Ridder, 'Relational discriminant analysis', Pattern Recognition Letters, vol. 20, pp. 1175 - 1181, 1999 https://doi.org/10.1016/S0167-8655(99)00085-9
  4. E. Pekalska and R. P. W. Duin, 'Dissimilarity representations allow for buiilding good classifiers, Pattern Recognition Letters, vol. 23, pp. 943 - 956, 2002 https://doi.org/10.1016/S0167-8655(02)00024-7
  5. Y. Horikawa, 'On properties of nearest neighbor classifiers for high-dimensional patterns in dissimilarity-based classification', IEICE Trans. Information & Systems, vol. J88-D-II, no. 4, pp. 813 - 817, Apr. 2005, in Japanese
  6. P. E. Hart, 'The condensed nearest neighbor rule', EEE Trans. Inform Theory, vol. IT-14, pp. 515 - 516, May 1968 https://doi.org/10.1109/TIT.1968.1054155
  7. C. L. Chang, 'Finding prototypes for nearest neighbor classifiers', IEEE Trans. Computers, vol. C-23, no. 11, pp. 1179 - 1184, Nov. 1974 https://doi.org/10.1109/T-C.1974.223827
  8. S. -W. Kim and B. J. Oommen, 'Enhancing prototype reduction schemes with LVQ3-type algorithms', Pattern Recognition, vol. 36, no. 5, pp. 1083 - 1093, 2003 https://doi.org/10.1016/S0031-3203(02)00115-2
  9. S. -W. Kim and B. J. Oommen, 'A taxonomy and ranking of creative prototype reduction schemes,' Pattern Analysis and Applications, Springer-Verlag, vol. 6, no. 3, pp. 232 - 244, December 2003 https://doi.org/10.1007/s10044-003-0191-0
  10. B. V. Dasarathy, Nearest Neighbour (NN) Norms: NN Pattern Classification Techniques, IEEE Computer Society Press, Los Alamitos, 1991
  11. J. C. Bezdek and L. I. Kuncheva, 'Nearest prototype classifier designs: An experimental study', International Journal of Intelligent Systems, vol. 16, no. 12, pp. 1445 - 1473, 2001 https://doi.org/10.1002/int.1068
  12. http://www.ics.uci.edu/~mlearn/MLRepository.html
  13. http://www-ai.cs.uni-dortmund.de/SOFTWARE/SVM_LIGHT/svm_light.eng.html