Asymmetric Gradient Boosting with Application to Spam Filtering

In this paper, we propose a new asymmetric boosting method, Boosting with Differerent Costs. Traditional boosting methods assume the same cost for misclassified instances from different classes, and in this way focus on good performance with respect to overall accuracy. Our method is more generic, and is designed to be more suitable for problems where the major concern is a low false positive (or negative) rate, such as spam filtering. Experimental results on a large scale email spam data set demonstrate the superiority of our method over state-of-the-art techniques.