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- W1515023925 abstract "Previous chapter Next chapter Full AccessProceedings Proceedings of the 2005 SIAM International Conference on Data Mining (SDM)On Error Correlation and Accuracy of Nearest Neighbor Ensemble ClassifiersCarlotta Domeniconi and Bojun YanCarlotta Domeniconi and Bojun Yanpp.217 - 226Chapter DOI:https://doi.org/10.1137/1.9781611972757.20PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Recent empirical work has shown that combining predictors can lead to significant reduction in generalization error. Unfortunately, many combining methods do not improve nearest neighbor (NN) classifiers at all. This is because NN methods are very robust with respect to variations of a data set. In contrast, they are sensitive to input features. We exploit the instability of NN classifiers with respect to different choices of features to generate an effective and diverse set of NN classifiers. Interestingly, the approach takes advantage of the high dimensionality of the data. We investigate techniques to decorrelate errors while keeping the individual classifiers accurate. We analyze the results both in terms of error rates and error correlations. The experimental results show that our technique can offer significant performance improvements with respect to competitive methods. Previous chapter Next chapter RelatedDetails Published:2005ISBN:978-0-89871-593-4eISBN:978-1-61197-275-7 https://doi.org/10.1137/1.9781611972757Book Series Name:ProceedingsBook Code:PR119Book Pages:xii + 648" @default.
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- W1515023925 title "On Error Correlation and Accuracy of Nearest Neighbor Ensemble Classifiers" @default.
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