Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 608)
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About this book
One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc.
Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD.
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Table of contents (22 chapters)
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Background and Foundation
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Instance Selection Methods
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Use of Sampling Methods
Editors and Affiliations
Bibliographic Information
Book Title: Instance Selection and Construction for Data Mining
Editors: Huan Liu, Hiroshi Motoda
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4757-3359-4
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media Dordrecht 2001
Hardcover ISBN: 978-0-7923-7209-7Published: 28 February 2001
Softcover ISBN: 978-1-4419-4861-8Published: 08 December 2010
eBook ISBN: 978-1-4757-3359-4Published: 09 March 2013
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XXV, 416
Topics: Data Structures and Information Theory, Artificial Intelligence, Information Storage and Retrieval, Statistics, general