Learning from automatically labeled data: case study on click fraud prediction-Reference-Cited by-同舟云学术

Learning from automatically labeled data: case study on click fraud prediction

Published:2015-02-26 Issue:2 Volume:46 Page:477-490
ISSN:0219-1377
Container-title:Knowledge and Information Systems
language:en
Short-container-title:Knowl Inf Syst

Author:

Berrar Daniel

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Link

http://link.springer.com/content/pdf/10.1007/s10115-015-0827-6.pdf

Reference19 articles.

1. Berrar D (2012) Random forests for the detection of click fraud in online mobile advertising. In: Proceedings of the 1st International Workshop on Fraud Detection in Mobile Advertising, pp. 1–10

2. Berrar D, Lozano J (2013) Significance tests or confidence intervals: which are preferable for the comparison of classifiers? J Exp Theor Artif Intell 25(2):189–206

3. Bootkrajang J, Kabán A (2013) Boosting in the presence of label noise. In: Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, pp. 82–90

4. Bouveyron C, Girard S (2009) Robust supervised classification with mixture models: learning from data with uncertain labels. Pattern Recognit 42:2649–2658

5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

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