Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Expert Systems with Applications: An International Journal
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Selecting Features and Objects for Mixed and Incomplete Data
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Finding Small Consistent Subset for the Nearest Neighbor Classifier Based on Support Graphs
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Using Maximum Similarity Graphs to Edit Nearest Neighbor Classifiers
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Information Sciences: an International Journal
Simultaneous features and objects selection for mixed and incomplete data
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Selecting prototypes in mixed incomplete data
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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Feature and instance selection before classification is a very important task, which can lead to big improvements in both classifier accuracy and classifier speed. However, few papers consider the simultaneous or combined instance and feature selection for Nearest Neighbor classifiers in a deterministic way. This paper proposes a novel deterministic feature and instance selection algorithm, which uses the recently introduced Minimum Neighborhood Rough Sets as basis for the selection process. The algorithm relies on a metadata computation to guide instance selection. The proposed algorithm deals with mixed and incomplete data and arbitrarily dissimilarity functions. Numerical experiments over repository databases were carried out to compare the proposal with respect to previous methods and to the classifier using the original sample. These experiments show the proposal has a good performance according to classifier accuracy and instance and feature reduction.