A combined component approach for finding collection-adapted ranking functions based on genetic programming

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

@InProceedings{conf/sigir/AlmeidaGCC07,

• author = "Humberto Mossri {de Almeida} and Marcos Andre Goncalves and Marco Cristo and Pavel Calado",
• title = "A combined component approach for finding collection-adapted ranking functions based on genetic programming",
• booktitle = "Proceedings of the 30th Annual International ACM Conference on Research and Development in Information Retrieval, SIGIR 2007",
• year = "2007",
• editor = "Wessel Kraaij and Arjen P. {de Vries} and Charles L. A. Clarke and Norbert Fuhr and Noriko Kando",
• pages = "399--406",
• address = "Amsterdam, The Netherlands",
• month = jul # " 23-27",
• publisher = "ACM",
• keywords = "genetic algorithms, genetic programming, Information Retrieval, Ranking Functions, Term-weighting, Machine Learning",
• isbn13 = "978-1-59593-597-7",
• DOI = "doi:10.1145/1277741.1277810",
• size = "8 pages",
• abstract = "In this paper, we propose a new method to discover collection-adapted ranking functions based on Genetic Programming (GP). Our Combined Component Approach (CCA)is based on the combination of several term-weighting components (i.e.,term frequency, collection frequency, normalization) extracted from well-known ranking functions. In contrast to related work, the GP terminals in our CCA are not based on simple statistical information of a document collection, but on meaningful, effective, and proven components. Experimental results show that our approach was able to out perform standard TF-IDF, BM25 and another GP-based approach in two different collections. CCA obtained improvements in mean average precision up to 40.87percent for the TREC-8 collection, and 24.85percent for the WBR99 collection (a large Brazilian Web collection), over the baseline functions. The CCA evolution process also was able to reduce the over training, commonly found in machine learning methods, especially genetic programming, and to converge faster than the other GP-based approach used for comparison.",
• bibdate = "2007-08-24",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sigir/sigir2007.html#AlmeidaGCC07",

}

Genetic Programming entries for Humberto Mossri de Almeida Marcos Andre Goncalves Marco Cristo Pavel Pereira Calado

Citations