A MapReduce based Parallel SVM for Large Scale Spam Filtering

摘要：

Spam continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) based techniques have been proposed for spam classification. However, SVM training is a computationally intensive process. This paper presents a parallel SVM algorithm for scalable spam filtering. By distributing, processing and optimizing the subsets of the training data across multiple participating nodes, the distributed SVM reduces the training time significantly. Ontology based concepts are also employed to minimize the impact of accuracy degradation when distributing the training data amongst the SVM classifiers.

关键词： Machine Learning Classification Ontology Semantics Support Vector Machine Parallel Computing

作者: Godwin Caruana Maozhen Li Man Qi

作者单位: School of Engineering and Design, Brunei University, Uxbridge, Middlesex, UB8 3PH, UK School of Engineering and Design, Brunei University, Uxbridge, Middlesex, UB8 3PH, UK The Key Labora Department of Computing, Canterbury Christ Church University, Canterbury, Kent, CT1 1QU, UK

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 2719-2722

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）