Learning to Detect Web Spam by Genetic Programming

摘要：

Web spam techniques enable some web pages or sites to achieve un deserved relevance and importance. They can seriously deteriorate search engine ranking results. Combating web spam has become one of the top chal lenges for web search. This paper proposes to learn a discriminating function to detect web spam by genetic programming. The evolution computation uses multi-populations composed of some small-scale individuals and combines the selected best individuals in every population to gain a possible best discriminat ing function. The experiments on WEBSPAM-UK2006 show that the approach can improve spam classification recall performance by 26％, F-measure per formance by 11％, and accuracy performance by 4％ compared with SVM.

关键词： Web Spam Information Retrieval Genetic Programming Machine Learning

作者: Xiaofei Niu Jun Ma Qiang He Shuaiqiang Wang Dongmei Zhang

作者单位: School of Computer Science and Technology, Shandong University, Jinan 250101, China School of Comput School of Computer Science and Technology, Shandong University, Jinan 250101, China Department of Computer Science, Texas State University, San Marcos, US

会议类型: 国际会议

会议名称: 11th International Conference,WAIM 2010(第十一届网络时代管理国际会议)

会议地点: 九寨沟

会议语种:英文

页码: 18-27

在线出版日期: 2010-07-14（万方平台首次上网日期，不代表论文的发表时间）