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Grow up precision recall relationship curve in IR system using GP and fuzzy optimization in optimizing the user query

dc.contributor.authorOwais, Suhail Sami Jebour
dc.contributor.authorSnášel, Václav
dc.contributor.authorKrömer, Pavel
dc.date.accessioned2007-10-10T13:27:22Z
dc.date.available2007-10-10T13:27:22Z
dc.date.issued2007
dc.identifier.citationNeural Network World. 2007, vol. 17, issue 4, p. 295-309.en
dc.identifier.issn1210-0552
dc.identifier.urihttp://hdl.handle.net/10084/63505
dc.language.isoenen
dc.publisherAkademie věd České republiky, Ústav informatikyen
dc.relation.ispartofseriesNeural Network Worlden
dc.subjectinformation retrievalen
dc.subjectgenetic programmingen
dc.subjectfuzzy optimizationen
dc.subjectBoolean operatoren
dc.subjectterm weightsen
dc.subjectprecisionen
dc.subjectrecall and harmonic meanen
dc.titleGrow up precision recall relationship curve in IR system using GP and fuzzy optimization in optimizing the user queryen
dc.typearticleen
dc.identifier.locationNení ve fondu ÚKen
dc.description.abstract-enAn information retrieval (IR) system (IRs) (search engine) is said to be efficient, to the degree that always evaluates each object in the information base (database, document base, web,...) like the expert. The ability of IRs's is to retrieve mostly all relevant objects (measured by the recall), and only the (most) relevant objects (measured by the precision) from the collection queried. Recall and precision measures provide the classical measure of the retrieval efficiency. They measure the degree to which the query answer (the set of documents that retrieved by IRs as response to the user query). Where, the query answer is the set of relevant documents in the information based queried. Retrieving most relevant documents to the user query in IRs was one of the most important methods of World Wide Web (WWW) search engines used in the world now. So the searchers aim to use genetic programming (GP) and fuzzy optimization to optimize the user search query in the Boolean IRs model and in the fuzzy IRs model; and to use more Boolean operators (AND, OR, XOR, OF, and NOT) instead of using the standard operators (AND, OR, and NOT), and to use weights for terms and for Boolean operators. Weights are used to give the users more relaxation in defining how much the importance of the terms and of the Boolean operators is. The terms and the Boolean operators' weights are used in fuzzy IRs model. In addition, it investigates extensions of the classical measurement of effectiveness in IRs, precision; recall and harmonic mean. The researchers use harmonic mean measure as an objective function which uses both measures precision and recall at once for evaluating the results of the two IRs models to grow up the precision-recall relationship curve.en
dc.identifier.wos000249076100004


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