An insight into imbalanced Big Data classification: outcomes and challenges-Reference-Cited by-同舟云学术

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An insight into imbalanced Big Data classification: outcomes and challenges

Published:2017-03-01 Issue:2 Volume:3 Page:105-120
ISSN:2199-4536
Container-title:Complex & Intelligent Systems
language:en
Short-container-title:Complex Intell. Syst.

Author:

Fernández Alberto^ORCID,del Río Sara,Chawla Nitesh V.,Herrera Francisco

Funder

Banco Bilbao Vizcaya Argentaria

Ministerio de Ciencia y Tecnología

Agencia de Innovación y Desarrollo de Andalucía

Division of Information and Intelligent Systems

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

http://link.springer.com/article/10.1007/s40747-017-0037-9/fulltext.html

Reference73 articles.

1. He H, García EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284

2. Sun Y, Wong AKC, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23(4):687–719

3. López V, Fernández A, García S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250(20):113–141

4. Krawczyk B (2016) Learning from imbalanced data: open challenges and future directions. Prog Artif Intell 5(4):221–232

5. Prati RC, Batista GEAPA, Silva DF (2015) Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Knowl Inf Syst 45(1):247–270

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