NDLI: Nearest neighbor ensemble

Content Provider	IEEE Xplore Digital Library
Author	Domeniconi, C. Yan, B.
Copyright Year	2004
Description	Author affiliation: Dept. of Inf. & Software Eng., George Mason Univ., Fairfax, VA, USA (Domeniconi, C.; Yan, B.)
Abstract	Recent empirical work has shown that combining predictors can lead to significant reduction in generalization error. The individual predictors (weak learners) can be very simple, such as two terminal-node trees; it is the aggregating scheme that gives them the power of increasing prediction accuracy. 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 with possibly uncorrelated errors. Interestingly, the approach takes advantage of the high dimensionality of the data. The experimental results show that our technique offers significant performance improvements with respect to competitive methods.
Starting Page	228
Ending Page	231
File Size	396640
Page Count	4
File Format	PDF
ISBN	0769521282
ISSN	10514651
DOI	10.1109/ICPR.2004.1334065
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Nearest neighbor searches Bagging Boosting Voting Neural networks Sampling methods Software engineering Computer errors Accuracy Robustness
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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