Using Multi-phase Cost-Sensitive Learning to Filtering Spam

This paper proposes a novel ensemble learning framework, namely, multiple-phase cost-sensitive ensemble learning (MPCSL) which simulates the means and process of human being learning.It consists of two types learning, i.e., direct learning which learns multiple weak learners from a training dataset via some homogeneous or heterogenous algorithms, and indirect learning that constructs a committee from the knowledge of the combined filters or other committees. This paper studies empirically the performance of MPCSL on spam filtering tasks.In the occasions of combining homogeneous and heterogeneous,how the performance of MPCSL changes is surveyed. The results shows that MPCSL is a compellent ensemble learning method for cost-sensitive tasks such as spam filtering.