A class boundary preserving algorithm for data condensation
Pattern Recognition
Instance selection for class imbalanced problems by means of selecting instances more than once
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Noisy data elimination using mutual k-nearest neighbor for classification mining
Journal of Systems and Software
InstanceRank based on borders for instance selection
Pattern Recognition
FRPS: A Fuzzy Rough Prototype Selection method
Pattern Recognition
Prototype reduction based on Direct Weighted Pruning
Pattern Recognition Letters
On the use of meta-learning for instance selection: An architecture and an experimental study
Information Sciences: an International Journal
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This paper presents a relational framework for studying properties of labeled data points related to proximity and labeling information in order to improve the performance of the 1NN rule. Specifically, the class conditional nearest neighbor (ccnn) relation over pairs of points in a labeled training set is introduced. For a given class label c, this relation associates to each point a its nearest neighbor computed among only those points with class label c (excluded a). A characterization of ccnn in terms of two graphs is given. These graphs are used for defining a novel scoring function over instances by means of an information-theoretic divergence measure applied to the degree distributions of these graphs. The scoring function is employed to develop an effective large margin instance selection method, which is empirically demonstrated to improve storage and accuracy performance of the 1NN rule on artificial and real-life data sets.