C4.5: programs for machine learning
C4.5: programs for machine learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Machine Learning
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Instance selection in text classification using the silhouette coefficient measure
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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In Pattern recognition, the supervised classifiers use a training set Tfor classifying new prototypes. In practice, not all information in Tis 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.