NDLI: Label Alteration to Improve Underwater Mine Classification

Content Provider	IEEE Xplore Digital Library
Author	Williams, D.P.
Copyright Year	2004
Abstract	A new algorithm for performing supervised classification that intentionally alters the training labels supplied with the data set is presented. The proposed approach is motivated by the insight that the average prediction of a group of sufficiently informed people is often more accurate than the prediction of any one supposed expert. This idea that the “wisdom of crowds” can outperform a single expert is implemented in two ways. When labeling error rates can be estimated, sets of labels are drawn as samples from a Bernoulli distribution. When side information is not available, or no labeling errors are suspected, labels are intentionally altered in a structured manner. The framework is demonstrated in the context of an underwater mine classification application on synthetic aperture sonar data collected at sea, with promising results.
Sponsorship	IEEE Geoscience and Remote Sensing Society
Starting Page	488
Ending Page	492
Page Count	5
File Size	263334
File Format	PDF
ISSN	1545598X
Volume Number	8
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Labeling Training data Synthetic aperture sonar Humans Remote sensing Training wisdom of crowds Classification ensemble methods mine detection underwater mines
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology

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