The object selection is an important task for instance-based classifiers since through this process the size of a training set could be reduced and then the runtimes in both classification and training steps would be reduced. Several methods for object selection have been proposed but some methods discard relevant objects for the classification step. In this paper, we propose an object selection method which is based on the idea of sequential floating search. This method reconsiders the inclusion of relevant objects previously discarded. Some experimental results obtained by our method are shown and compared against some other object selection methods. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Olvera-López, J. A., Martínez-Trinidad, J. F., & Carrasco-Ochoa, J. A. (2007). Restricted sequential floating search applied to object selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4571 LNAI, pp. 694–702). Springer Verlag. https://doi.org/10.1007/978-3-540-73499-4_52
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