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Title of Journal: Soft Comput

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Abbravation: Soft Computing

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Springer-Verlag

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10.1016/0014-3057(84)90201-5

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1433-7479

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An evolutionary algorithm to discover quantitative

Authors: M MartínezBallesteros F MartínezÁlvarez A Troncoso J C Riquelme
Publish Date: 2011/03/22
Volume: 15, Issue: 10, Pages: 2065-
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Abstract

An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work The proposed model to discover these relationships is based on quantitative association rules This algorithm called QARGA Quantitative Association Rules by Genetic Algorithm uses a particular codification of the individuals that allows solving two basic problems First it does not perform a previous attribute discretization and second it is not necessary to set which variables belong to the antecedent or consequent Therefore it may discover all underlying dependencies among different variables To evaluate the proposed algorithm three experiments have been carried out As initial step several public datasets have been analyzed with the purpose of comparing with other existing evolutionary approaches Also the algorithm has been applied to synthetic time series where the relationships are known to analyze its potential for discovering rules in time series Finally a realworld multidimensional time series composed by several climatological variables has been considered All the results show a remarkable performance of QARGAThe financial support from the Spanish Ministry of Science and Technology project TIN200768084C02 and from the Junta de Andalucía project P07TIC02611 is acknowledged The authors also want to acknowledge the support by the Regional Ministry for the Environment Consejería de Medio Ambiente of Andalucía Spain that has provided all the pollutant agents time series

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