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

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O. Cordón, E. Herrera-Viedma, M. Luque, F. Moya, C. Zarco: Analyzing the performance of a multiobjective GA-P algorithm for learning fuzzy queries in a machine learning environment. in: International Fuzzy Systems Association World Congress, 2003, Istanbul, Turkey, LNAI 2715



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.

Extended Abstract


author="Cord{\'o}n, Oscar and Herrera-Viedma, Enrique and Luque, Mar{\'i}a and de Moya, F{\'e}lix and Zarco, Carmen",
editor="Bilgi{\c{c}}, Taner and De Baets, Bernard and Kaynak, Okyay",
title="Analyzing the Performance of a Multiobjective GA-P Algorithm for Learning Fuzzy Queries in a Machine Learning Environment",
bookTitle="Fuzzy Sets and Systems --- IFSA 2003: 10th International Fuzzy Systems Association World Congress Istanbul, Turkey, June 30 -- July 2, 2003 Proceedings",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",

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