Object Selection Based on Clustering and Border Objects
Object selection is an important task 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 method based on clustering which tries to find border objects that contribute with useful information allowing to the classifier discriminating between classes. An experimental comparison of our method, the CLU method based on clustering, and the DROP methods, is presented.
Unable to display preview. Download preview PDF.
- 7.Blake, C., Keogh, E., Merz, C. (1998). UCI repository of machine learning databases [http://www.ics.uci.edu/ mlearn/MLRepository.html]. Department of Information and Computer Science, University of California, Irvine, CA.Google Scholar