Title :
Fuzzy genes: improving the effectiveness of information retrieval
Author :
Martín-Bautista, Maria J. ; Vila, Maria-Amparo ; Sánchez, Daniel ; Larsen, Henrik L.
Author_Institution :
Dept. de Ciencias de la Comput. e Inteligencia Artificial, Granada Univ., Spain
Abstract :
An improvement in the effectiveness of information retrieval by using genetic algorithms (GAs) and fuzzy logic is demonstrated. A new classification of information retrieval models within the framework of GAs is given. Such a classification is based on the target of the fitness function selected. When the aim of the optimization is document classification, we deal with document-oriented models. On the other hand, term-oriented models attempt to find those terms that are more discriminatory and adequate for user preferences to build a profile. A new weighting scheme based on fuzzy logic is presented for the first class of models. A comparison with other classical weighting schemes and a study of the best aggregation operators of the gene´s local fitness to the overall fitness per chromosome are also presented. The deeper study of this new scheme in the term-oriented models is the main objective for future work
Keywords :
classification; fuzzy logic; genetic algorithms; information retrieval; mathematical operators; user modelling; aggregation operators; chromosomes; discriminatory terms; document classification; document-oriented models; fitness function; fuzzy genes; fuzzy logic; genetic algorithms; information retrieval effectiveness; information retrieval models; local fitness; optimization; term-oriented models; user preferences; user profile; weighting scheme; Artificial intelligence; Biological cells; Computer science; Fuzzy logic; Genetic algorithms; Genetic mutations; Indexing; Information retrieval; Rough sets; Search methods;
Conference_Titel :
Evolutionary Computation, 2000. Proceedings of the 2000 Congress on
Conference_Location :
La Jolla, CA
Print_ISBN :
0-7803-6375-2
DOI :
10.1109/CEC.2000.870334