A novel approach integrating ranking functions discovery, optimization and infernce to improve retrieval performance

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dc.contributor.authorAgbele, Kehinde K.
dc.contributor.authorAdesina, Ademola Olusola
dc.contributor.authorNyongesa, Henry O.
dc.contributor.authorFebba, Ronald
dc.date.accessioned2014-03-19T14:25:43Z
dc.date.available2014-03-19T14:25:43Z
dc.date.issued2010
dc.description.abstractThe significant roles play by ranking function in the performance and success of Information Retrieval (IR) systems and search engines cannot be underestimated. Diverse ranking functions are available in IR literature. However, empirical studies show that ranking functions do not perform constantly well across different contexts (queries, collections, users). In this study, a novel three-stage integrated ranking framework is proposed for implementing discovering, optimizing and inference rankings used in IR systems. The first phase, discovery process is based on Genetic Programming (GP) approach which smartly combines structural and contents features in the documents while the second phase, optimization process is based on Genetic Algorithm (GA) which combines document retrieval scores of various well-known ranking functions. In the 3rd phase, Fuzzy inference proves as soft search constraints to be applied on documents. We demonstrate how these two features are combined to bring new tasks and processes within the three concept stages of integrated framework for effective IR.en_US
dc.identifier.citationAgbele, K.K., et al. (2010). A novel approach integrating ranking functions discovery, optimization and infernce to improve retrieval performance. International Journal of Soft Computing, 5(3): 155-163en_US
dc.identifier.issn1816-9503
dc.identifier.urihttp://hdl.handle.net/10566/1062
dc.language.isoenen_US
dc.privacy.showsubmitterFALSE
dc.publisherMedwell Journalsen_US
dc.rights© 2010 Agbele et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.status.ispeerreviewedTRUE
dc.subjectRanking functionen_US
dc.subjectInformation retrievalen_US
dc.subjectEvolutionary techniquesen_US
dc.subjectFuzzy inference systemen_US
dc.subjectData fusion methoden_US
dc.titleA novel approach integrating ranking functions discovery, optimization and infernce to improve retrieval performanceen_US
dc.typeArticleen_US

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