Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

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

DOI QR Code

An Improvement of AdaBoost using Boundary Classifier

  • Lee, Wonju (School of Electrical and Electronic Engineering, Yonsei University) ;
  • Cheon, Minkyu (School of Electrical and Electronic Engineering, Yonsei University) ;
  • Hyun, Chang-Ho (Division of Electrical Electronic and Control Engineering, Kongju National University) ;
  • Park, Mignon (School of Electrical and Electronic Engineering, Yonsei University)
  • Received : 2013.04.02
  • Accepted : 2013.04.05
  • Published : 2013.04.25
Download PDF
⟨ Previous Next ⟩

Abstract

The method proposed in this paper can improve the performance of the Boosting algorithm in machine learning. The proposed Boundary AdaBoost algorithm can make up for the weak points of Normal binary classifier using threshold boundary concepts. The new proposed boundary can be located near the threshold of the binary classifier. The proposed algorithm improves classification in areas where Normal binary classifier is weak. Thus, the optimal boundary final classifier can decrease error rates classified with more reasonable features. Finally, this paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Boundary AdaBoost in a simulation experiment of pedestrian detection using 10-fold cross validation.

Keywords

References

  1. Y. Freund and R. E. Schapire, "A Short Introduction to Boosting," Journal of Japanese Society for Artificial Intelligence, Vol. 14, No. 5, pp. 771-780, 1999.
  2. R. E. Schapire and Y. Singer, "Improved Boosting Algorithms Using Confidence-rated Predictions," Machine Learning, Vol. 37, No. 3. pp. 297-335, 1999. https://doi.org/10.1023/A:1007614523901
  3. J. Friedman, T. Hastie and R. Tibshirani, "Additive Logistic Regression: a Statistical View of Boosting," The Annals of Statistics, Vol. 28, No. 2, pp. 337-407, 2000.
  4. A. Vezhnevets and V. Vezhnevets, "Modest AdaBoost - Teaching AdaBoost to Generalize Better," Graphicon, 2005.
  5. R. E. Schapire, "Theoretical Views of Boosting and Applications," In Proceedings of Algorithmic Learning Theory, Vol. 1720, pp. 13-25, 1999.
  6. Y. Freund and R. E. Schapire, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," Journal of Computer and System Sciences, Vol. 55, No. 1, pp. 119-139, 1997. https://doi.org/10.1006/jcss.1997.1504
  7. R. E. Schapire and Y. Singer, "BoosTexter: A Boosting-Based System for Text Categorization," Machine Learning, Vol. 39, No. 2/3, pp. 135-168, 2000. https://doi.org/10.1023/A:1007649029923
  8. Y. Freund, "Boosting a Weak Learning Algorithm by Majority," Information and Computation, Vol. 121, No. 2, pp. 256-285, 1995. https://doi.org/10.1006/inco.1995.1136
  9. W. W. Cohen, "Fast Effective Rule Induction," In Proceedings of Machine Learning, pp. 115-123, 1995.
  10. W. W. Cohen and Y. Singer, "A Simple, Fast, and Effective Rule Learner," In Proceedings of Artificial Intelligence, pp. 335-342, 1999.
  11. H. Drucker and C. Cortes, "Boosting Decision Trees," In Advances in Neural Information Processing Systems, Vol. 8, pp. 479-485, 1996.
  12. Y. Freund, "An Adaptive Version of the Boost by Majority Algorithm," In Proceedings of Computational Learning Theory, pp. 102-113, 1999.
  13. R. Maclin and D. Opitz, "An Empirical Evaluation of Bagging and Boosting," In Proceedings of Artificial Intelligence, pp. 546-551, 1997.
  14. Takagi and M. Sugeno, "Fuzzy Identification of Systems and its Applications to Modeling and Control," On Systems, Man, Cybernetics, Vol. 15, No. 1, pp. 116-132, 1985.
  15. D. Singer and P. G. Singer, "System Identification Based on Linguistic Variables," Fuzzy Sets and Systems, Vol. 47, No. 2, pp. 141-149, 1992. https://doi.org/10.1016/0165-0114(92)90171-Y
  16. K. Shahida, Ibraheem, Moinuddin and M. Farooq, "A Table Lookup Scheme for Fuzzy Logic Based Model Identification Applied to Time Series Prediction," In Proceedings of Information Fusion, Vol. 2, pp. 1449-1456, 2003.
  17. Q. Zhu, S. Avidan, Y. Mei-Chen and C. Kwang-Ting, "Fast Human Detection Using a Cascade of Histograms of Oriented Gradients," In Proceedings of Computer Vision and Pattern Recognition, Vol. 2, pp. 1491-1498, 2006.
  18. F. Suard, A. Rakotomamonjy, A. Bensrhair and A. Broggi, "Pedestrian Detection using Infrared images and Histograms of Oriented Gradients," In Proceedings of Intelligent Vehicles Symposium, pp. 206-212, 2006.
  19. Y. Yamauchi, H. Fujiyoshi, B. Hwang and T. Kanade, "People Detection Based on Co-Occurrence of Appearance and Spatiotemporal Features," In Proceedings of Pattern Recognition, pp. 1-4, 2008.

Cited by

  1. A Study on Chaff Echo Detection using AdaBoost Algorithm and Radar Data vol.23, pp.6, 2013, https://doi.org/10.5391/JKIIS.2013.23.6.545