Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Intelligent Data Analysis XXI - 21st International Symposium on Intelligent Data Analysis, IDA 2023, Proceedings |
| Editors | Bruno Crémilleux, Sibylle Hess, Siegfried Nijssen |
| Pages | 392-405 |
| Number of pages | 14 |
| Publication status | Published - 2023 |
| Externally published | Yes |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13876 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data. Generally, one is interested in finding an anomaly detector that correctly identifies anomalies, i.e., data points that do not belong to the normal class, without raising too many false alarms. Which anomaly detector is best suited depends on the dataset at hand and thus needs to be tailored. The quality of an anomaly detector may be assessed via confusion-based metrics such as the Matthews correlation coefficient (MCC). However, since during training only normal data is available in a semi-supervised setting, such metrics are not accessible. To facilitate automated machine learning for anomaly detectors, we propose to employ meta-learning to predict MCC scores using the metrics that can be computed with normal data only and order anomaly detectors using the predicted scores for selection. First promising results can be obtained considering the hypervolume and the false positive rate as meta-features.
Keywords
- Anomaly Detection, AutoML, Meta-learning
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Advances in Intelligent Data Analysis XXI - 21st International Symposium on Intelligent Data Analysis, IDA 2023, Proceedings. ed. / Bruno Crémilleux; Sibylle Hess; Siegfried Nijssen. 2023. p. 392-405 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13876 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets.
AU - Schubert, David
AU - Gupta, Pritha
AU - Wever, Marcel
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data. Generally, one is interested in finding an anomaly detector that correctly identifies anomalies, i.e., data points that do not belong to the normal class, without raising too many false alarms. Which anomaly detector is best suited depends on the dataset at hand and thus needs to be tailored. The quality of an anomaly detector may be assessed via confusion-based metrics such as the Matthews correlation coefficient (MCC). However, since during training only normal data is available in a semi-supervised setting, such metrics are not accessible. To facilitate automated machine learning for anomaly detectors, we propose to employ meta-learning to predict MCC scores using the metrics that can be computed with normal data only and order anomaly detectors using the predicted scores for selection. First promising results can be obtained considering the hypervolume and the false positive rate as meta-features.
AB - In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data. Generally, one is interested in finding an anomaly detector that correctly identifies anomalies, i.e., data points that do not belong to the normal class, without raising too many false alarms. Which anomaly detector is best suited depends on the dataset at hand and thus needs to be tailored. The quality of an anomaly detector may be assessed via confusion-based metrics such as the Matthews correlation coefficient (MCC). However, since during training only normal data is available in a semi-supervised setting, such metrics are not accessible. To facilitate automated machine learning for anomaly detectors, we propose to employ meta-learning to predict MCC scores using the metrics that can be computed with normal data only and order anomaly detectors using the predicted scores for selection. First promising results can be obtained considering the hypervolume and the false positive rate as meta-features.
KW - Anomaly Detection
KW - AutoML
KW - Meta-learning
UR - http://www.scopus.com/inward/record.url?scp=85152517433&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-30047-9_31
DO - 10.1007/978-3-031-30047-9_31
M3 - Conference contribution
SN - 9783031300462
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 392
EP - 405
BT - Advances in Intelligent Data Analysis XXI - 21st International Symposium on Intelligent Data Analysis, IDA 2023, Proceedings
A2 - Crémilleux, Bruno
A2 - Hess, Sibylle
A2 - Nijssen, Siegfried
ER -