Learning to Detect Web Spam by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/waim/NiuMHWZ10,

• title = "Learning to Detect Web Spam by Genetic Programming",
• author = "Xiaofei Niu and Jun Ma and Qiang He and Shuaiqiang Wang and Dongmei Zhang",
• booktitle = "Web-Age Information Management, 11th International Conference, {WAIM} 2010, Jiuzhaigou, China, July 15-17, 2010. Proceedings",
• publisher = "Springer",
• year = "2010",
• volume = "6184",
• editor = "Lei Chen and Changjie Tang and Jun Yang and Yunjun Gao",
• isbn13 = "978-3-642-14245-1",
• pages = "18--27",
• series = "Lecture Notes in Computer Science",
• URL = "http://dx.doi.org/10.1007/978-3-642-14246-8",
• DOI = "doi:10.1007/978-3-642-14246-8_5",
• keywords = "genetic algorithms, genetic programming",
• abstract = "Web spam techniques enable some web pages or sites to achieve undeserved relevance and importance. They can seriously deteriorate search engine ranking results. Combating web spam has become one of the top challenges 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 discriminating function. The experiments on WEBSPAM-UK2006 show that the approach can improve spam classification recall performance by 26percent, F-measure performance by 11percent, and accuracy performance by 4percent compared with SVM.",
• bibdate = "2010-07-07",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/waim/waim2010.html#NiuMHWZ10",

}

Genetic Programming entries for Xiaofei Niu Jun Ma Qiang He Shuaiqiang Wang Dongmei Zhang

Citations