Hellinger distance decision trees are robust and skew-insensitive-Reference-Cited by-同舟云学术

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Hellinger distance decision trees are robust and skew-insensitive

Published:2011-06-09 Issue:1 Volume:24 Page:136-158
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Cieslak David A.,Hoens T. Ryan,Chawla Nitesh V.,Kegelmeyer W. Philip

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s10618-011-0222-1.pdf

Reference41 articles.

1. Alpaydin E (1999) Combined 5 × 2cv F test for comparing supervised classification learning algorithms. Neural Comput 11(8): 1885–1892

2. Asuncion A, Newman D (2007) UCI machine learning repository. http://www.ics.uci.edu/~mlearn/MLRepository.html

3. Banfield R, Hall LO, Bowyer KW, Kegelmeyer WP (2007) A comparison of decision tree ensemble creation techniques. IEEE Trans Pattern Anal Mach Intell 29(1): 832–844

4. Batista G, Prati R, Monard M (2004) A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor 6(1): 20–29

5. Breiman L (1996) Bagging predictors. Mach Learn 24(2): 123–140

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