Edition is an important and useful task in supervised classification specifically for instance-based classifiers because edition discards from the training set those useless or harmful objects for the classification accuracy and it helps to reduce the size of the original training sample and to increase both the classification speed and accuracy. In this paper, we propose two edition schemes that combine edition methods and sequential search for instance selection. In addition, we present an empirical comparison between these schemes and some other edition methods. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Olvera-López, J. A., Martínez-Trinidad, J. F., & Carrasco-Ochoa, J. A. (2005). Edition schemes based on BSE. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3773 LNCS, pp. 360–367). Springer Verlag. https://doi.org/10.1007/11578079_38
Mendeley helps you to discover research relevant for your work.