Machine learning by multi-feature extraction using genetic algorithms
EntityUAM. Departamento de Ingeniería Informática
PublisherSpringer Berlin Heidelberg
10.1007/978-3-540-30498-2_25Advances in Artificial Intelligence – IBERAMIA 2004: 9th Ibero-American Conference on AI, Puebla, Mexico, November 22-26, 2004. Proceedings. Lecture Notes in Computer Science, Volumen 3315. Springer, 2004. 246-255
ISSN0302-9743 (print); 1611-3349 (online)
ISBN978-3-540-30498-2 (online); 978-3-540-23806-5 (print)
Funded byThis work has been partially supported by the Spanish Interdepartmental Commission for Science and Technology (CICYT), under Grant number TIC2002-1948
SubjectsArtificial Intelligence; Computation by Abstract Devices; Image Processing and Computer Vision; Informática
NoteThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-30498-2_25
Proceedings of 9th Ibero-American Conference on AI, Puebla, Mexico, November 22-26, 2004.
Rights© Springer-Verlag Berlin Heidelberg 2004
Constructive Induction methods aim to solve the problem of learning hard concepts despite complex interaction in data. We propose a new Constructive Induction method based on Genetic Algorithms with a non-algebraic representation of features. The advantage of our method to some other similar methods is that it constructs and evaluates a combination of features. Evaluating constructed features together, instead of considering them one by one, is essential when number of interacting attributes is high and there are more than one interaction in concept. Our experiments show the effectiveness of this method to learn such concepts.
Google Scholar:Shafti, Leila Shila - Pérez, Eduardo
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Constructive induction and genetic algorithms for learning concepts with complex interaction Shafti, Leila Shila; Pérez, Eduardo
Genetic approach to constructive induction based on non-algebraic feature representation Shafti, Leila Shila; Pérez, Eduardo