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Learning from Imbalanced Data Sets

Produktform: Buch / Einband - flex.(Paperback)

This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. weiterlesen

Sprache(n): Englisch

ISBN: 978-3-030-07446-3 / 978-3030074463 / 9783030074463

Verlag: Springer International Publishing

Erscheinungsdatum: 19.01.2019

Seiten: 377

Auflage: 1

Autor(en): Salvador García, Francisco Herrera, Mikel Galar, Alberto Fernández, Ronaldo C. Prati, Bartosz Krawczyk

160,49 € inkl. MwSt.
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lieferbar - Lieferzeit 10-15 Werktage

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