User behavior prediction in social networks using weighted extreme learning machine with distribution optimization

With the increasing presence of online social networks (OSN), there is a growing interest in accurately predicting user behaviors based on the data collected from OSN. Unlike traditional data, data collected from OSN are often imbalanced, which could make the user behavior prediction based on such data challenging. In this article, a novel data-driven method is proposed using extreme learning machine (ELM). ELM uses a single layer feedforward network (SLFN), and it has been attracting strong attentions in the research community because of its fast training speed and good generalization performance. However, the traditional ELM is mainly designed for balanced data learning in which the overall distribution of data is not considered. Hence, the prediction accuracy of ELM may be worse than some traditional neural network (NN) learning algorithms for some practical tasks. To address this issue, we enhance ELM by considering the distribution of data through the use of L2 norm. Specifically, the weighted strategy is integrated into our proposed method by setting weight for each class based on its size. For social network imbalanced data, some experiments are conducted using our weighted ELM based on the overall distribution, named ODW-ELM, to deal with binary classification and multi-class classification tasks. The experimental results show that ODW-ELM can achieve better performance in balanced datasets and imbalanced datasets. And ODW-ELM can also achieve better performance for classification application in social network while classifying blogs according to their popularity.