@inproceedings{4957c25e03d346ad8c7fffce1d94a29b,
title = "Semi-supervised outlier detection",
abstract = "Outlier detection has been extensively researched in the context of unsupervised learning. But the learning results are not always satisfactory, which can be significantly improved using supervision of some labeled points. In this paper, we are concerned with employing supervision of limited amount of label information to detect outliers more accurately. The key of our approach is an objective function that punishes poor clustering results and deviation from known labels as well as restricts the number of outliers. The outliers can be found as a solution to the discrete optimization problem regarding the objective function. By this way, this method can detect meaningful outliers that can not be identified by existing unsupervised methods.",
keywords = "Outlier detection, Semi-supervised learning",
author = "Jing Gao and Haibin Cheng and Tan, \{Pang Ning\}",
year = "2006",
doi = "10.1145/1141277.1141421",
language = "English",
isbn = "1595931082",
series = "Proceedings of the ACM Symposium on Applied Computing",
publisher = "Association for Computing Machinery ",
pages = "635--636",
booktitle = "Applied Computing 2006 - The 21st Annual ACM Symposium on Applied Computing - Proceedings of the 2006 ACM Symposium on Applied Computing",
address = "United States",
note = "2006 ACM Symposium on Applied Computing ; Conference date: 23-04-2006 Through 27-04-2006",
}