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<datestamp>2017-12-21</datestamp>
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<title lang=en>An evolutionary data mining approach on hydrological data with classifier juries</title>
<creator>Segretier, Wilfried</creator>
<creator>Clergue, Manuel</creator>
<creator>Collard, Martine</creator>
<creator>Izquierdo, Luis</creator>
<contributor>IDC ; Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG) - Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>Universidad de La Habana (CUBA) ; Universidad de La Habana (CUBA)</contributor>
<description>International audience</description>
<source>IEEE Congress on Evolutionary Computation 2012</source>
<coverage>Brisbane, Australia</coverage>
<publisher>IEEE</publisher>
<identifier>hal-00840739</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00840739</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00840739</source>
<source>IEEE Congress on Evolutionary Computation 2012, Jun 2013, Brisbane, Australia. IEEE, pp.1-8, 2012, 〈10.1109/CEC.2012.6252897〉</source>
<identifier>DOI : 10.1109/CEC.2012.6252897</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1109/CEC.2012.6252897</relation>
<language>en</language>
<subject lang=en>Evolutionary Computation</subject>
<subject lang=en>Flood prediction</subject>
<subject lang=en>Data mining</subject>
<subject>[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>In this paper, we present an evolutionary approach for extracting a model of flood prediction from hydrological data observed timely on water heights in a river watershed. Since this kind of data recorded by sensors on river basins is highly scarce and hopefully much unbalanced between cases of floods and non-floods, we have adopted the notion of aggregate variables which values are computed as aggregates on raw data. An evolutionary algorithm is involved to allow selecting the best sets - juries of classifiers- of such variables as predictive variables. Two real hydrological data sets are trained and they both show the efficiency of the method compared to traditional solutions for prediction.</description>
<date>2013-06-10</date>
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