Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 453)
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About this book
There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem.
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Table of contents (24 chapters)
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Front Matter
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Background and Foundation
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Front Matter
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Subset Selection
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Front Matter
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Feature Extraction
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Front Matter
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Feature Construction
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Front Matter
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Editors and Affiliations
Accessibility Information
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Bibliographic Information
Book Title: Feature Extraction, Construction and Selection
Book Subtitle: A Data Mining Perspective
Editors: Huan Liu, Hiroshi Motoda
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-5725-8
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1998
Hardcover ISBN: 978-0-7923-8196-9Published: 31 August 1998
Softcover ISBN: 978-1-4613-7622-4Published: 11 October 2012
eBook ISBN: 978-1-4615-5725-8Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: XXIV, 410
Topics: Data Structures and Information Theory, Statistics, general, Artificial Intelligence