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.
This work was financially supported by CONACyT (Mexico) through the project J38707-A.
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© 2005 Springer-Verlag Berlin Heidelberg
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Olvera-López, J.A., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2005). Sequential Search for Decremental Edition. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_37
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DOI: https://doi.org/10.1007/11508069_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
Online ISBN: 978-3-540-31693-0
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