Title :
Genetic algorithm based dynamic parameter learning for text retrieval
Author :
Lin, Chuan ; Ma, Shao-Ping ; Zhang, Min ; Jin, Yi-jiang
Author_Institution :
CST Dept., Tsinghua Univ., Beijing, China
Abstract :
In information retrieval (IR) systems, such as Okapi, there are always a variety of parameters to be set manually which are data-dependent and most sensitive to retrieval performance. Therefore, it will be ideal to deploy an automatic parameter learning mechanism. In this paper, we propose such a method based on the genetic algorithm. We apply our approach to the Okapi system. Experimental results on TREC2001 testing data indicate that our algorithm is effective to adjust system parameters and improve the retrieval performance significantly.
Keywords :
genetic algorithms; information retrieval; learning (artificial intelligence); Okapi system; TREC2001 testing data; fitness function; genetic algorithm; information retrieval; machine learning; parameter learning; Biological cells; Evolutionary computation; Feedback; Genetic algorithms; Information retrieval; Learning systems; Machine learning; Space technology; System testing; Training data;
Conference_Titel :
Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
Print_ISBN :
0-7803-7508-4
DOI :
10.1109/ICMLC.2002.1174538
