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A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.

Goldstein M, Uchida S - PLoS ONE (2016)

Bottom Line: Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets.Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time.As a conclusion, we give an advise on algorithm selection for typical real-world tasks.

View Article: PubMed Central - PubMed

Affiliation: Center for Co-Evolutional Social System Innovation, Kyushu University, Fukuoka, Japan.

ABSTRACT
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.

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Related in: MedlinePlus

Different anomaly detection modes depending on the availability of labels in the dataset.(a) Supervised anomaly detection uses a fully labeled dataset for training. (b) Semi-supervised anomaly detection uses an anomaly-free training dataset. Afterwards, deviations in the test data from that normal model are used to detect anomalies. (c) Unsupervised anomaly detection algorithms use only intrinsic information of the data in order to detect instances deviating from the majority of the data.
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pone.0152173.g001: Different anomaly detection modes depending on the availability of labels in the dataset.(a) Supervised anomaly detection uses a fully labeled dataset for training. (b) Semi-supervised anomaly detection uses an anomaly-free training dataset. Afterwards, deviations in the test data from that normal model are used to detect anomalies. (c) Unsupervised anomaly detection algorithms use only intrinsic information of the data in order to detect instances deviating from the majority of the data.

Mentions: In contrast to the well-known classification setup, where training data is used to train a classifier and test data measures performance afterwards, there are multiple setups possible when talking about anomaly detection. Basically, the anomaly detection setup to be used depends on the labels available in the dataset and we can distinguish between three main types as illustrated in Fig 1:


A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.

Goldstein M, Uchida S - PLoS ONE (2016)

Different anomaly detection modes depending on the availability of labels in the dataset.(a) Supervised anomaly detection uses a fully labeled dataset for training. (b) Semi-supervised anomaly detection uses an anomaly-free training dataset. Afterwards, deviations in the test data from that normal model are used to detect anomalies. (c) Unsupervised anomaly detection algorithms use only intrinsic information of the data in order to detect instances deviating from the majority of the data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836738&req=5

pone.0152173.g001: Different anomaly detection modes depending on the availability of labels in the dataset.(a) Supervised anomaly detection uses a fully labeled dataset for training. (b) Semi-supervised anomaly detection uses an anomaly-free training dataset. Afterwards, deviations in the test data from that normal model are used to detect anomalies. (c) Unsupervised anomaly detection algorithms use only intrinsic information of the data in order to detect instances deviating from the majority of the data.
Mentions: In contrast to the well-known classification setup, where training data is used to train a classifier and test data measures performance afterwards, there are multiple setups possible when talking about anomaly detection. Basically, the anomaly detection setup to be used depends on the labels available in the dataset and we can distinguish between three main types as illustrated in Fig 1:

Bottom Line: Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets.Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time.As a conclusion, we give an advise on algorithm selection for typical real-world tasks.

View Article: PubMed Central - PubMed

Affiliation: Center for Co-Evolutional Social System Innovation, Kyushu University, Fukuoka, Japan.

ABSTRACT
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.

Show MeSH
Related in: MedlinePlus