Imbalanced Learning: Foundations, Algorithms, and Applications - Rilegato

 
9781118074626: Imbalanced Learning: Foundations, Algorithms, and Applications
Rilegato
ISBN 10: 1118074629 ISBN 13: 9781118074626
Casa editrice: IEEE, 2013

Sinossi

The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning

Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation.

The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on:

  • Foundations of Imbalanced Learning
  • Imbalanced Datasets: From Sampling to Classifiers
  • Ensemble Methods for Class Imbalance Learning
  • Class Imbalance Learning Methods for Support Vector Machines
  • Class Imbalance and Active Learning
  • Nonstationary Stream Data Learning with Imbalanced Class Distribution
  • Assessment Metrics for Imbalanced Learning

Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

HAIBO HE, PhD, is an Associate Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island. He received the National Science Foundation (NSF) CAREER Award and Providence Business News (PBN) Rising Star Innovator Award.

YUNQIAN MA PhD, is a senior principal research scientist of Honeywell Labs at Honeywell Inter-national, Inc. He received the International Neural Network Society (INNS) Young Investigator Award.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
IEEE
Data di pubblicazione
2013
Lingua
Inglese
ISBN 10 
1118074629
ISBN 13 
9781118074626
Rilegatura
Copertina rigida
Numero edizione
1
Numero di pagine
210
Redattore
He Haibo, Ma Yunqian
Contatto del produttore
non disponibile
Persona responsabile
non disponibile