Abstract
An efficient immune query optimization algorithm for information retrieval is proposed in this paper. The main characteristics of this algorithm are as follows: The genetic individual is a query, each gene corresponds to a weighted term, immune operator is used to avoid degeneracy, local search procedure based on the concept of neighborhood is used to speed up the abilities of finding better query vector. Experimental results show that the proposed algorithm can efficiently improve the performance of the query search.
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References
Chen, H.: Machine learning for information retrieval:neural networks, symbolic learning and genetic algorithms. Journal of the American Society for Information Science 46, 194–216 (1995)
Horng, J.T., Yeh, C.C.: Applying genetic algorithms to query optimization in document retrieval. Information Processing and Management 36, 737–759 (2000)
Boughanem, M., Chrisment, C., Tamine, L.: Genetic approach to query space exploration. Information Retrieval 1, 175–192 (1999)
Jiao, L.C., Wang, L.: A novel genetic algorithm based on immunity. IEEE Transactions on Systems, Man, and Cybernetics-Part A 30, 552–561 (2000)
Singhal, A., Buckley, C., Mitra, M.: Pivoted document length normalisation. In: Proc. of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Zurich, Switzerland, pp. 21–29. ACM Press, New York (1996)
Harman, D.K.: Overview of the first text retrieval conference (TREC-1). In: Proc. of the 1st Text Retrieval Conference, Gaitherburg, USA, pp. 32–59 (1992)
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Wang, Z., Feng, B. (2004). Optimal Genetic Query Algorithm for Information Retrieval. In: Cao, J., Yang, L.T., Guo, M., Lau, F. (eds) Parallel and Distributed Processing and Applications. ISPA 2004. Lecture Notes in Computer Science, vol 3358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30566-8_102
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DOI: https://doi.org/10.1007/978-3-540-30566-8_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24128-7
Online ISBN: 978-3-540-30566-8
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