Intelligent filtering with genetic algorithms and fuzzy logic

Authors:
María J. Martín-Bautista;María-Amparo Vila;Daniel Sánchez;Henrik L. Larsen
Affiliations:
Dept. of Computer Science and Artificial Intelligence, Granada University, Avda. Andalucía 37, 18071 Granada, Spain;Dept. of Computer Science and Artificial Intelligence, Granada University, Avda. Andalucía 37, 18071 Granada, Spain;Dept. of Computer Science and Artificial Intelligence, Granada University, Avda. Andalucía 37, 18071 Granada, Spain;Dept. of Computer Science, Roskilde University, P.O. Box 260, DK-4000 Roskilde, Denmark
Venue:
Technologies for constructing intelligent systems
Year:
2002

Citing 12
Cited 3

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Abstract

We present two different approaches combining fuzzy information retrieval of documents with genetic algorithms, and the pre-processing stage of classification called feature selection. The differences between these approaches lie basically in the target of the fitness function selected. In the first approach, the Term-Oriented Model, the fitness function is based on a measure to find the most discriminatory terms, by rewarding not only the terms from the good documents, but also those from the bad ones, if they are considered as good partial classifiers. However, the aim of the Document-Oriented Model, as traditionally, is to rank the documents by relevance. So, the best chromosome represents the optimal query. The fuzzy weighting scheme used in this model considers also the discriminatory terms by introducing the knowledge about the user preferences in the genes, but rewarding the genes belonging to the good documents.