One of the major challenging tasks in E-Commerce is the prediction and classification of commercial data. Boosting is a popular ensemble learning method and can improve results of classification algorithms. In this paper, a boosting decision trees algorithm is proposed to analyze and predict the commercial data. An experimental evaluation is carried out on public commercial dataset and the experimental results show that the proposed method improves performance of prediction obviously.
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