Genetic approach to constructive induction based on non-algebraic feature representation
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
Springer Berlin HeidelbergFecha de edición
2003Cita
10.1007/978-3-540-45231-7_55
Advances in Intelligent Data Analysis V: 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003. Proceedings. Lecture Notes in Computer Science, Volumen 2810. Springer, 2003. 599-610
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-540-40813-0 (print); 978-3-540-45231-7 (online)DOI
10.1007/978-3-540-45231-7_55Financiado por
This work has been partially supported by the Spanish Interdepartmental Commission for Science and Technology (CICYT), under Grants numbers TIC98-0247-C02-02 and TIC2002-1948.Versión del editor
http://dx.doi.org/10.1007/978-3-540-45231-7_55Materias
Information Storage and Retrieval; Probability and Statistics in Computer Science; Artificial Intelligence; Pattern Recognition; Business Information Systems; Computer Applications in Administrative Data Processing; InformáticaNota
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-45231-7_55Proceedings of 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003
Derechos
© Springer-Verlag Berlin Heidelberg 2003Resumen
The aim of constructive induction (CI) is to transform the original data representation of hard concepts with complex interaction into one that outlines the relation among attributes. CI methods based on greedy search suffer from the local optima problem because of high variation in the search space of hard learning problems. To reduce the local optima problem, we propose a CI method based on genetic (evolutionary) algorithms. The method comprises two integrated genetic algorithms to construct functions over subsets of attributes in order to highlight regularities for the learner. Using non-algebraic representation for constructed functions assigns an equal degree of complexity to functions. This reduces the difficulty of constructing complex features. Experiments show that our method is comparable with and in some cases superior to existing CI methods.
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Google Scholar:Shafti, Leila Shila
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Pérez, Eduardo
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