Restricted Sequential Floating Search Applied to Object Selection

Authors:
J. Arturo Olvera-López;J. Francisco Martínez-Trinidad;J. Ariel Carrasco-Ochoa
Affiliations:
Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP: 72840, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP: 72840, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP: 72840, Mexico
Venue:
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2007

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Abstract

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.