Overview
- Offers a comprehensive overview of ensemble learning in the field of feature selection (FS)
- Provides the user with the background and tools needed to develop new ensemble methods for feature selection
- Reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance
- Shows examples of problems in which ensembles for feature selection have been successfully applied, and introduces the new challenges and possibilities that researchers now face
Part of the book series: Intelligent Systems Reference Library (ISRL, volume 147)
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Table of contents (10 chapters)
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Front Matter
Keywords
About this book
This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance.
With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative.
The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges thatresearchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining.
Authors and Affiliations
Bibliographic Information
Book Title: Recent Advances in Ensembles for Feature Selection
Authors: Verónica Bolón-Canedo, Amparo Alonso-Betanzos
Series Title: Intelligent Systems Reference Library
DOI: https://doi.org/10.1007/978-3-319-90080-3
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-90079-7Published: 14 May 2018
Softcover ISBN: 978-3-030-07929-1Published: 30 January 2019
eBook ISBN: 978-3-319-90080-3Published: 30 April 2018
Series ISSN: 1868-4394
Series E-ISSN: 1868-4408
Edition Number: 1
Number of Pages: XIV, 205
Number of Illustrations: 3 b/w illustrations, 36 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Pattern Recognition