Survey on deep learning with class imbalance

Author:

Johnson Justin M.,Khoshgoftaar Taghi M.

Publisher

Springer Science and Business Media LLC

Subject

Information Systems and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems

Reference131 articles.

1. Witten IH, Frank E, Hall MA, Pal CJ. Data mining, Fourth Edition: Practical machine learning tools and techniques. 4th ed. San Francisco: Morgan Kaufmann Publishers Inc.; 2016.

2. Kotsiantis SB. Supervised machine learning: a review of classification techniques. In: Proceedings of the 2007 conference on emerging artificial intelligence applications in computer engineering: Real Word AI Systems with applications in eHealth, HCI, Information Retrieval and Pervasive Technologies. IOS Press, Amsterdam, The Netherlands, The Netherlands; 2007. p. 3–24. http://dl.acm.org/citation.cfm?id=1566770.1566773 . Accessed 15 Oct 2018.

3. Rao RB, Krishnan S, Niculescu RS. Data mining for improved cardiac care. SIGKDD Explor Newsl. 2006;8(1):3–10. https://doi.org/10.1145/1147234.1147236 .

4. Wei W, Li J, Cao L, Ou Y, Chen J. Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web. 2013;16(4):449–75. https://doi.org/10.1007/s11280-012-0178-0 .

5. Herland M, Khoshgoftaar TM, Bauder RA. Big data fraud detection using multiple medicare data sources. J Big Data. 2018;5(1):29. https://doi.org/10.1186/s40537-018-0138-3 .

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