AAAI Publications, Twenty-Seventh AAAI Conference on Artificial Intelligence

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

Last modified: 2013-06-30


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.


Anomaly Detection, Outlier Detection, Mixed-type data; Robust estimation

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