Document (#27758)

Author

Lopez-Pujalte, C.
Guerrero Bote, V.P.
Moya-Anegón, F. de

Title

Evaluation of the application of genetic algorithms to relevance feedback

Source

Challenges in knowledge representation and organization for the 21st century: Integration of knowledge across boundaries. Proceedings of the 7th ISKO International Conference Granada, Spain, July 10-13, 2002. Ed.: M. López-Huertas

Imprint

Würzburg : Ergon Verlag

Year

2003

Pages

S.422-428

Series

Advances in knowledge organization; vol.8

Abstract

We evaluated the different genetic algorithms applied to relevance feedback that are to be found in the literature and which follow the vector space model (the most commonly used model in this type of application). They were compared with a traditional relevance feedback algorithm - the Ide dec-hi method - since this had given the best results in the study of Salton & Buckley (1990) an this subject. The experiment was performed an the Cranfield collection, and the different algorithms were evaluated using the residual collection method (one of the most suitable methods for evaluating relevance feedback techniques). The results varied greatly depending an the fitness function that was used, from no improvement in some of the genetic algorithms, to a more than 127% improvement with one algorithm, surpassing even the traditional Ide dec-hi method. One can therefore conclude that genetic algorithms show great promise as an aid to implementing a truly effective information retrieval system.

Theme

Retrievalalgorithmen

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2. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.45

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3. López-Pujalte, C.; Guerrero-Bote, V.P.; Moya-Anegón, F. de: Order-based fitness functions for genetic algorithms applied to relevance feedback (2003) 0.44

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4. Smith, M.P.; Smith, M.: ¬The use of genetic programming to build Boolean queries for text retrieval through relevance feedback (1997) 0.43

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5. Robertson, A.M.; Willett, P.: Use of genetic algorithms in information retrieval (1995) 0.40

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