The KIPS Transactions:PartD (정보처리학회논문지D)

Korea Information Processing Society (한국정보처리학회)
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Splitting Rules using Intervals for Object Classification in Image Databases

이미지 데이터베이스에서 인터벌을 이용한 객체분류를 위한 분리 방법

  • 조준서 (한국외국어대학교 경영학부) ;
  • 최준수 (국민대학교 자연과학대학 컴퓨터학부)
  • Published : 2005.12.01
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Abstract

The way to assign a splitting criterion for correct object classification is the main issue in all decisions trees. This paper describes new splitting rules for classification in order to find an optimal split point. Unlike the current splitting rules that are provided by searching all threshold values, this paper proposes the splitting rules that we based on the probabilities of pre assigned intervals. Our methodology provides that user can control the accuracy of tree by adjusting the number of intervals. In addition, we applied the proposed splitting rules to a set of image data that was retrieved by parameterized feature extraction to recognize image objects.

정확한 객체 분류를 위한 분리 기준을 지정하기 위한 방법은 의사결정수들(decisions trees) 사이에 주요한 이슈이다. 이 논문은 최적의 분리 지점을 찾기 위해 분류에 대한 새로운 분리방법을 기술하고 있다. 모든 시작점 값들(threshold values)을 검색함으로 제공되는 기존의 분리방법들과 다르게, 이 논문에서는 미리 지정된 인터벌들의 확률을 기반으로 하는 분리방법을 제안하고 있다 제시한 방법은 사용자가 인터벌의 수를 조정함으로써 트리의 정확도를 통제할 수 있도록 할 수 있으며, 제기된 분리방법을 이미지 객체 인식하기 위한 특징추출을 수치화함으로써 검색되는 일련의 이미지 데이터에 적용하였다.

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References

  1. J. R. Beck and E. K Schultz. The use of ROC curves in test performance evaluation. Arch Pathol Lab Med, 1986
  2. P. Bradely, The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997 https://doi.org/10.1016/S0031-3203(96)00142-2
  3. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth, 1984
  4. Wray Buntine. Learning Classification Trees. Statistics and Computing, 1992 https://doi.org/10.1007/BF01889584
  5. June-Suh Cho. Feature Extraction of Shape of Image Objects in Content-based Image Retrieval. The KIPS Transactions: Part B, 2003
  6. L. A. Clark and D. Pregibon. Tree-based models, in J. M. Chambers and T. J. Hastie(eds), Statistical Models in S. Chapman and Hall, New York, 1993
  7. B. Efron. Estimating the error rate of a prediction rule: Improvement on cross-validation. Journal of the American Statistical Association, 1983 https://doi.org/10.2307/2288636
  8. Johannes Gehrke, Venkatesh Ganti, Raghu Ramakrishnan, and Wei-Yin Loh. BOAT-Optimistic Decision Tree Construction. In SIGMOD'99, 1999 https://doi.org/10.1145/304182.304197
  9. Johannes Gehrke, Raghu Ramakrishnan, and Venkatesh Ganti. RainForest - A Framework for Fast Decision Tree Construction of Large Datasets. In Proceedings or the 24th VLDB Conference, New York, 1998
  10. Rodney M. Goodman and Padhraic J. Smyth. Decision tree design from a communication theory standpoint. IEEE Transactions on Information Theory, 1988 https://doi.org/10.1109/18.21221
  11. D. M. Hawkins and G. V. Kass. Automatic Interaction Detection. Cambridge University Press, 1982
  12. George H. John. Robust linear discriminant trees. In The 5th International Workshop on Artificial Intelligence and Statistics, 1995
  13. T. Lim and W. Loh. A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms. Dept. of Statistics in University of Wisconsin Technical Report 979, 1999
  14. Wei-Yin Loh and Yu-Shan Shih. Split selection methods for classification trees. Statistica Sinica, 1997
  15. Sreerama K. Murthy. On Growing Better Decision Trees From Data. Ph.D. Thesis in Johns Hopkins University, 1995
  16. Sreerama K. Murthy, Simon Kasif, and Steven Salzberg. A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, 1994
  17. Foster Provost, Tom Fawcett, and Ron Kohavi. The Case Against Accuracy Estimation for Comparing Induction Algorithms. In International Conference on Machine Learning, 1998
  18. J. R Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993
  19. R. Rastogi and K. Shim. PUBLIC A Decision Tree Classifier that Integrates Building and Pruning. In Proceedings of the 24nd VLDB Conference, 1998