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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 27 / No. 1: Twenty-Seventh AAAI Conference on Artificial Intelligence

A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection

March 8, 2023

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Authors

Yen-Cheng Lu

Virginia Tech


Feng Chen

Carnegie Mellon University


Yang Chen

Virginia Tech


Chang-Tien Lu

Virginia Tech


DOI:

10.1609/aaai.v27i1.8581


Abstract:

Anomaly detection for mixed-type data is an important problem that has not been well addressed in the machine learning field. There are two challenging issues for mixed-type datasets, namely modeling mutual correlations between mixed-type attributes and capturing large variations due to anomalies. This paper presents BuffDetect, a robust error buffering approach for anomaly detection in mixed-type datasets. A new variant of the generalized linear model is proposed to model the dependency between mixed-type attributes. The model incorporates an error buffering component based on Student-t distribution to absorb the variations caused by anomalies. However, because of the non- Gaussian design, the problem becomes analytically intractable. We propose a novel Bayesian inference approach, which integrates Laplace approximation and several computational optimizations, and is able to efficiently approximate the posterior of high dimensional latent variables by iteratively updating the latent variables in groups. Extensive experimental evaluations based on 13 benchmark datasets demonstrate the effectiveness and efficiency of BuffDetect.

Topics: AAAI

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HOW TO CITE:

Yen-Cheng Lu|| Feng Chen|| Yang Chen|| Chang-Tien Lu A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection Proceedings of the AAAI Conference on Artificial Intelligence, 27 (2013) 633.

Yen-Cheng Lu|| Feng Chen|| Yang Chen|| Chang-Tien Lu A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection AAAI 2013, 633.

Yen-Cheng Lu|| Feng Chen|| Yang Chen|| Chang-Tien Lu (2013). A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 27, 633.

Yen-Cheng Lu|| Feng Chen|| Yang Chen|| Chang-Tien Lu. A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 27 2013 p.633.

Yen-Cheng Lu|| Feng Chen|| Yang Chen|| Chang-Tien Lu. 2013. A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection. "Proceedings of the AAAI Conference on Artificial Intelligence, 27". 633.

Yen-Cheng Lu|| Feng Chen|| Yang Chen|| Chang-Tien Lu. (2013) "A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection", Proceedings of the AAAI Conference on Artificial Intelligence, 27, p.633

Yen-Cheng Lu|| Feng Chen|| Yang Chen|| Chang-Tien Lu, "A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection", AAAI, p.633, 2013.

Yen-Cheng Lu|| Feng Chen|| Yang Chen|| Chang-Tien Lu. "A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection". Proceedings of the AAAI Conference on Artificial Intelligence, 27, 2013, p.633.

Yen-Cheng Lu|| Feng Chen|| Yang Chen|| Chang-Tien Lu. "A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection". Proceedings of the AAAI Conference on Artificial Intelligence, 27, (2013): 633.

Yen-Cheng Lu|| Feng Chen|| Yang Chen|| Chang-Tien Lu. A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection. AAAI[Internet]. 2013[cited 2023]; 633.


ISSN: 2374-3468


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