Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
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COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECML '00 Proceedings of the 11th European Conference on Machine Learning
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PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
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The Journal of Machine Learning Research
Efficient Nearest Neighbor Classification Using a Cascade of Approximate Similarity Measures
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Boosting Nearest Neighbor Classi.ers for Multiclass Recognition
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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ICWE '9 Proceedings of the 9th International Conference on Web Engineering
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PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
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ACC'09 Proceedings of the 2009 conference on American Control Conference
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Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
K nearest neighbor reinforced expectation maximization method
Expert Systems with Applications: An International Journal
Investigating a novel GA-based feature selection method using improved KNN classifiers
International Journal of Information and Communication Technology
Noisy data elimination using mutual k-nearest neighbor for classification mining
Journal of Systems and Software
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Computers in Biology and Medicine
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SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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Knowledge-Based Systems
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The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. Past experience has shown that the optimal choice of Kdepends upon the data, making it laborious to tune the parameter for different applications. We introduce a new metric that measures the informativeness of objects to be classified. When applied as a query-based distance metric to measure the closeness between objects, two novel KNN procedures, Locally Informative-KNN (LI-KNN) and Globally Informative-KNN (GI-KNN), are proposed. By selecting a subset of most informative objects from neighborhoods, our methods exhibit stability to the change of input parameters, number of neighbors(K) and informative points (I). Experiments on UCI benchmark data and diverse real-world data sets indicate that our approaches are application-independent and can generally outperform several popular KNN extensions, as well as SVM and Boosting methods.