ML Anthology

ConOut: Contextual Outlier Detection with Multiple Contexts: Application to Ad Fraud

Meghanath Macha Yadagiri, Deepak Pai, Leman Akoglu
ECML-PKDD 2018 pp. 139-156
doi:10.1007/978-3-030-10925-7_9 /ecmlpkdd/2018/yadagiri2018ecmlpkdd-conout/

Abstract

Outlier detection has numerous applications in different domains. A family of techniques, called contextual outlier detectors, are based on a single , user-specified demarcation of data attributes into indicators and contexts. In this work, we propose ConOut , a new contextual outlier detection technique that leverages multiple contexts that are automatically identified. Importantly, ConOut  is a one-click algorithm—it does not require any user-specified (hyper)parameters. Through experiments on various real-world data sets, we show that ConOut outperforms existing baselines in detection accuracy. Further, we motivate and apply ConOut to the advertisement domain to identify fraudulent publishers, where ConOut not only improves detection but also provides statistically significant revenue gains to advertisers: a minimum of 57% compared to a naïve fraud detector; and $\sim $ 20% in revenue gains as well as $\sim $ 34% in mean average precision compared to its nearest competitor. Code related to this paper is available at: https://github.com/meghanathmacha/ConOut , https://cmuconout.github.io/ .

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Cite

Text

Yadagiri et al. "ConOut: Contextual Outlier Detection with Multiple Contexts: Application to Ad Fraud." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2018. doi:10.1007/978-3-030-10925-7_9

Markdown

[Yadagiri et al. "ConOut: Contextual Outlier Detection with Multiple Contexts: Application to Ad Fraud." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2018.](https://mlanthology.org/ecmlpkdd/2018/yadagiri2018ecmlpkdd-conout/) doi:10.1007/978-3-030-10925-7_9

BibTeX

@inproceedings{yadagiri2018ecmlpkdd-conout,
  title     = {{ConOut: Contextual Outlier Detection with Multiple Contexts: Application to Ad Fraud}},
  author    = {Yadagiri, Meghanath Macha and Pai, Deepak and Akoglu, Leman},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2018},
  pages     = {139-156},
  doi       = {10.1007/978-3-030-10925-7_9},
  url       = {https://mlanthology.org/ecmlpkdd/2018/yadagiri2018ecmlpkdd-conout/}
}