Spam detection on social networks using cost-sensitive feature selection and ensemble-based regularized deep neural networks

Spam detection on social networks is increasingly important owing to the rapid growth of social network user base. Sophisticated spam filters must be developed to deal with this complex problem. Traditional machine learning approaches such as neural networks, support vector machines and Naive Bayes classifiers are not effective enough to process and utilize complex features present in high-dimensional data on social network spam. Moreover, the traditional objective criteria of social network spam filters cannot cope with different costs assigned to type I and type II errors. To overcome these problems, here we propose a novel cost-sensitive approach to social network spam filtering. The proposed approach is composed of two stages. In the first stage, multi-objective evolutionary feature selection is used to minimize both the misclassification cost of the proposed model and the number of attributes necessary for spam filtering. Then, the approach uses cost-sensitive ensemble learning techniques with regularized deep neural networks as base learners. We demonstrate that this approach is effective for social network spam filtering on two benchmark datasets. We also show that the proposed approach outperforms other popular algorithms used in social network spam filtering, such as random forest, Naive Bayes or support vector machines.