Document Type
Article
Abstract
The annoyance of spam increasingly plagues both individuals and organizations. Spam classification is an important issue to distinguish the spam with the legitimate email or address. This paper presents a neural network ensemble approach based on a specially designed cooperative coevolution paradigm. Each component network corresponds to a separate subpopulation and all subpopulations are evolved simultaneously. The ensemble performance and the Q-statistic diversity measure are adopted as the objectives, and the component networks are evaluated by using the multi-objective Pareto optimality measure. Experimental results illustrate that the proposed algorithm outperforms the traditional ensemble methods on the spam classification problems.
Recommended Citation
Tian, Jin; Li, Minqiang; and Chen, Fuzan, "An Effective Ensemble Approach for Spam Classification" (2010). ICEB 2010 Proceedings (Shanghai, China). 25.
https://aisel.aisnet.org/iceb2010/25