Mixed Data Object Selection Based on Clustering and Border Objects [chapter]

J. Arturo Olvera-López, J. Francisco Martínez-Trinidad, J. Ariel Carrasco-Ochoa
Lecture Notes in Computer Science  
In supervised classification, the object selection or instance selection is an important task, mainly for instance-based classifiers since through this process the time in training and classification stages could be reduced. In this work, we propose a new mixed data object selection method based on clustering and border objects. We carried out an experimental comparison between our method and other object selection methods using some mixed data classifiers.
doi:10.1007/978-3-540-76725-1_70 dblp:conf/ciarp/Olvera-LopezTC07 fatcat:eo6ahw66njf4fm64uf77yf6oee