Prototype Selection Via Prototype Relevance
In Pattern recognition, the supervised classifiers use a training set T for classifying new prototypes. In practice, not all information in T is useful for classification therefore it is necessary to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time in the training and/or classification stages could be reduced. Several prototype selection methods have been proposed following the Nearest Neighbor (NN) rule; in this work, we propose a new prototype selection method based on the prototype relevance and border prototypes, which is faster (over large datasets) than the other tested prototype selection methods. We report experimental results showing the effectiveness of our method and compare accuracy and runtimes against other prototype selection methods.
KeywordsPrototype selection border prototypes supervised classification data reduction
- 5.Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
- 10.Chien-Hsing, C., Bo-Han, K., Fu, C.: The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method. In: 18th International Conference on Pattern Recognition, vol. 2, pp. 556–559. IEEE press, Washington (2006)Google Scholar
- 12.Devijver, P.A., Kittler, J.: On the edited nearest neighbor rule. In: 5th International Conference on Pattern Recognition. The Institute of Electrical and Electronics Engineers, pp. 72–80 (1980)Google Scholar
- 14.UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine CA, http://www.ics.uci.edu/~mlearn/MLRepository.html