Analyzing the performance of a multiobjective GA-P algorithm for learning fuzzy queries in a machine learning environment

Authors:
Oscar Cordón;Enrique Herrera-Viedma;María Luque;Félix de Moya;Carmen Zarco
Affiliations:
Dept. of Computer Science and A.I. University of Granada, Granada, Spain;Dept. of Computer Science and A.I. University of Granada, Granada, Spain;Dept. of Computer Science and A.I. University of Granada, Granada, Spain;Dept. of Library and Information Science. University of Granada, Granada, Spain;PULEVA Food S.A. Camino de Purchil, Granada, Spain
Venue:
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Year:
2003

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Abstract

The fuzzy information retrieval model was proposed some years ago to solve several limitations of the Boolean model without a need of a complete redesign of the information retrieval system. However, the complexity of the fuzzy query language makes it difficult to formulate user queries. Among other proposed approaches to solve this problem, we find the Inductive Query by Example (IQBE) framework, where queries are automatically derived from sets of documents provided by the user. In this work we test the applicability of a multiobjective evolutionary IQBE technique for fuzzy queries in a machine learning environment. To do so, the Cranfield documentary collection is divided into two different document sets, labeled training and test, and the algorithm is run on the former to obtain several queries that are then validated on the latter.