%0 Conference Paper %1 Zhang05c %A Zhang, Baoping %A Chen, Yuxin %A Fan, Weiguo %A Fox, Edward A. %A Goncalves, Marcos Andre %A Cristo, Marco %A Calado, Pavel %B Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval %C Salvador, Brazil %D 2005 %I ACM Press %K Poster algorithms, genetic programming, %P 667--668 %T Intelligent fusion of structural and citation-based evidence for text classification %U http://doi.acm.org/10.1145/1076034.1076181 %X This paper shows how different measures of similarity derived from the citation information and the structural content (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than other fusion techniques. %@ 1-59593-034-5

@inproceedings{Zhang05c, abstract = {This paper shows how different measures of similarity derived from the citation information and the structural content (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than other fusion techniques.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Salvador, Brazil}, author = {Zhang, Baoping and Chen, Yuxin and Fan, Weiguo and Fox, Edward A. and Goncalves, Marcos Andre and Cristo, Marco and Calado, Pavel}, biburl = {https://www.bibsonomy.org/bibtex/2b42a27489f046c9162e4a280c0b432ad/brazovayeye}, booktitle = {Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval}, copyright = {Copyright is held by the author/owner.}, interhash = {86599829d5e0073e999b15164ec8f1d8}, intrahash = {b42a27489f046c9162e4a280c0b432ad}, isbn = {1-59593-034-5}, keywords = {Poster algorithms, genetic programming,}, month = {August 15-19}, mrnumber = {C.IR.05.667}, notes = {See also \cite{Zhang05cTR}}, organisation = {SIGIR: ACM Special Interest Group on Information Retrieval}, pages = {667--668}, publisher = {ACM Press}, publisher_address = {New York, NY, USA}, size = {2 pages}, timestamp = {2008-06-19T17:55:12.000+0200}, title = {Intelligent fusion of structural and citation-based evidence for text classification}, url = {http://doi.acm.org/10.1145/1076034.1076181}, year = 2005 }