Design for Fast Adaboost with Feature Selection

Abstract:

Since the original Adaboost algorithm is very time-consuming in training, we have designed an improved Adaboost, which adds a process of feature selection to the original Adaboost algorithm. After each round of training, we retain the features whose error rate to classify the samples are relatively low and remove the features with high error rate. In this way the time for the next training is reduced, and the whole algorithm is accelerated.

• Periodical: Advanced Materials Research (Volumes 816-817)
• Pages: 566-569
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.816-817.566
• Online since: September 2013
• Authors: Xiao Long Liang, Wei Hua Li, Xin Lin
• Keywords: Adaboost, Feature Selection, Machine Learning (ML)

References

[1] K. Levi and Y. Weiss. Learning object detection from a small number of examples: The importance of good features. In Proc. CVPR (Washington, USA, June 27- July 2, 2004). Vol. 2, pp.53-60.

DOI: 10.1109/cvpr.2004.1315144

[2] A. Blum and P. Langley. Selection of relevant features and examples in machine learning. Artificial Intelligence Vol. 97(1997), pp.245-271.

DOI: 10.1016/s0004-3702(97)00063-5

[3] Friedman J, Hastie T, Tibshirani R. Additiv logistic gression; A statistical view of boosting. The Annals of Statistics Vol. 28(2000), pp.337-407.

[4] D. Redpath and K. Lebart. Observations on boosting feature selection. In Proc. Multiple Classifier Systems (Seaside, USA, June 13-15, 2005). Vol. 3541, pp.32-41.

DOI: 10.1007/11494683_4

[5] R. Collins, Y. Liu, and M. Leordeanu. Online selection of discriminative tracking features. Pattern Analysis and Machine Intelligence, Vol. 27(2005), pp.1631-1643.

DOI: 10.1109/tpami.2005.205

[6] Freund Y. Boosting a weak learning algorithm by majority. Informatin and Computation Vol. 121(1995), pp.256-285.

DOI: 10.1006/inco.1995.1136

[7] R. Schapire, Y. Freund, P. Bartlett, and W. Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. In Proc. ICML (Madison, USA, July 24-27, 1998). Vol. 26, pp.1651-1686.

DOI: 10.1214/aos/1024691352

[8] R. Schapire. The boosting approach to machine learning: An overview. In Proc. MSRI Workshop on Nonlinear Estimation and Classification (Berkeley, USA, March, 2001).

[9] Schapire R E, Singer Y. BoosTexter: A boosting-based system for text categorization. Machine Learning Vol. 39(2000), pp.135-168.

[10] Viola P, Jones M. Robust real-time face detection. International Journal of Computer Vision Vol. 57(2004), pp.137-154.

DOI: 10.1023/b:visi.0000013087.49260.fb

[11] A. Opelt, A. Pinz, M. Fussenegger. Generic object recognition with boosting. Pattern Analysis and Machine Intelligence Vol. 28(2006), pp.416-431.

DOI: 10.1109/tpami.2006.54