A novel multiclass AdaBoost-based extreme learning machine (ELM) ensemble algorithm is proposed, in which the weighted ELM is selected as the basic weak classifier because of its much faster learning speed and much better generalisation performance than traditional support vector machines. AdaBoost acts as an ensemble learning method of a number of weighted ELMs. Then, an ensemble strong classifier is constructed by the weighted majority vote of all the weighted ELMs. Compared with the existing ELM methods, the proposed algorithm solves the problem of how to train the weighted samples by ELM in multiclass classification directly. Experiments on the German Traffic Sign Recognition Benchmark database demonstrate that the proposed algorithm can achieve a high recognition accuracy of 99.12% with a relatively lower computational complexity than many state-of-the-art algorithms.

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computational complexity multiclass adaboostbased extreme learning machine elm ensemble algorithm stateoftheart algorithms weighted elms ensemble strong classifier multiclass classification support vector machines adaboost majority vote generalisation algorithm recognition accuracy german traffic sign recognition benchmark database

Quanwei Wang,Yan Xu, Zhenyu Wei, Shuo Ma,.Traffic sign recognition based on weighted ELM and AdaBoost. 52 (24),1988-1990.

Quanwei Wang, et al."Traffic sign recognition based on weighted ELM and AdaBoost" 52

Quanwei Wang,Yan Xu, Zhenyu Wei, Shuo Ma,"Traffic sign recognition based on weighted ELM and AdaBoost"