Abstract
In this paper a new evolutionary multi-objective algorithm (GAR-SD) for Subgroup Discovery tasks is presented. This algorithm can work with both discrete and continuous attributes without the need for a previous discretization. An experimental study was carried out to verify the performance of the method. GAR-SD was compared with other subgroup discovery methods by evaluating certain measures (such as number of rules, number of attributes, significance, support and confidence). For Subgroup Discovery tasks, GAR-SD obtained good results compared with existing algorithms.
This work was partially funded by the Spanish Ministry of Science and Innovation, the Spanish Government Plan E and the European Union through ERDF (TIN2009-14057- C03-03).
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Pachón, V., Mata, J., Domínguez, J.L., Maña, M.J. (2011). Multi-objective Evolutionary Approach for Subgroup Discovery. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_33
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DOI: https://doi.org/10.1007/978-3-642-21222-2_33
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