Speeding up Boosting decision trees training

Chao Zheng; Zhenzhong Wei

doi:10.1117/12.2197329

8 October 2015 Speeding up Boosting decision trees training

Chao Zheng, Zhenzhong Wei

Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96750F (2015) https://doi.org/10.1117/12.2197329
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China

Abstract

To overcome the drawback that Boosting decision trees perform fast speed in the test time while the training process is relatively too slow to meet the requirements of applications with real-time learning, we propose a fast decision trees training method by pruning those noneffective features in advance. And basing on this method, we also design a fast Boosting decision trees training algorithm. Firstly, we analyze the structure of each decision trees node, and prove that the classification error of each node has a bound through derivation. Then, by using the error boundary to prune non-effective features in the early stage, we greatly accelerate the decision tree training process, and would not affect the training results at all. Finally, the decision tree accelerated training method is integrated into the general Boosting process forming a fast boosting decision trees training algorithm. This algorithm is not a new variant of Boosting, on the contrary, it should be used in conjunction with existing Boosting algorithms to achieve more training acceleration. To test the algorithm’s speedup performance and performance combined with other accelerated algorithms, the original AdaBoost and two typical acceleration algorithms LazyBoost and StochasticBoost were respectively used in conjunction with this algorithm into three fast versions, and their classification performance was tested by using the Lsis face database which contained 12788 images. Experimental results reveal that this fast algorithm can achieve more than double training speedup without affecting the results of the trained classifier, and can be combined with other acceleration algorithms. Key words: Boosting algorithm, decision trees, classifier training, preliminary classification error, face detection

Citation Download Citation

Chao Zheng and Zhenzhong Wei "Speeding up Boosting decision trees training", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750F (8 October 2015); https://doi.org/10.1117/12.2197329

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KEYWORDS

Error analysis

MATLAB

Databases

Detection and tracking algorithms

Facial recognition systems

Image classification

Chaos

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