Traffic sign recognition based on weighted ELM and AdaBoost
Yan Hui Xu, Quanwei Wang, Zhenyu Wei, Shuo Ma · Electronics Letters · 2016
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.