Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Data squashing: constructing summary data sets
Handbook of massive data sets
Inference for the Generalization Error
Machine Learning
Pruning classification rules with reference vector selection methods
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
| Hi-index | 0.00 |
Instance selection methods are very useful data mining tools for dealing with large data sets. There exist many instance selection algorithms capable for significant reduction of training data size for particular classifier without generalization degradation. In opposition to those methods, this paper focuses on general pruning methods which can be successfully applied for arbitrary classification method. Simple but efficient wrapper method based on generalization of Hart's Condensed Nearest Neighbors rule is presented and impact of this method on classification quality is reported.