Sequential search for decremental edition

Authors:
José A. Olvera-López;J. Ariel Carrasco-Ochoa;José Fco. Martínez-Trinidad
Affiliations:
Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Sta María Tonanzintla, Puebla, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Sta María Tonanzintla, Puebla, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Sta María Tonanzintla, Puebla, Mexico
Venue:
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Year:
2005

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Abstract

The edition process is an important task in supervised classification because it helps to reduce the size of the training sample. On the other hand, Instance-Based classifiers store all the training set indiscriminately, which in almost all times, contains useless or harmful objects, for the classification process. Therefore it is important to delete unnecessary objects to increase both classification speed and accuracy. In this paper, we propose an edition method based on sequential search and we present an empirical comparison between it and some other decremental edition methods.