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Detection of Anomalies in Large Datasets Using an Active Learning Scheme Based on Dirichlet Distributions

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 5290)


Today, the detection of anomalous records is a highly valuable application in the analysis of current huge datasets. In this paper we propose a new algorithm that, with the help of a human expert, efficiently explores a dataset with the goal of detecting relevant anomalous records. Under this scheme the computer selectively asks the expert for data labeling, looking for relevant semantic feedback in order to improve its knowledge about what characterizes a relevant anomaly. Our rationale is that while computers can process huge amounts of low level data, an expert has high level semantic knowledge to efficiently lead the search. We build upon our previous work based on Bayesian networks that provides an initial set of potential anomalies. In this paper, we augment this approach with an active learning scheme based on the clustering properties of Dirichlet distributions. We test the performance of our algorithm using synthetic and real datasets. Our results indicate that, under noisy data and anomalies presenting regular patterns, our approach significantly reduces the rate of false positives, while decreasing the time to reach the relevant anomalies.


  • Active Learning
  • Bayesian Network
  • Anomaly Detection
  • Synthetic Dataset
  • Dirichlet Distribution

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  • DOI: 10.1007/978-3-540-88309-8_17
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Pichara, K., Soto, A., Araneda, A. (2008). Detection of Anomalies in Large Datasets Using an Active Learning Scheme Based on Dirichlet Distributions. In: Geffner, H., Prada, R., Machado Alexandre, I., David, N. (eds) Advances in Artificial Intelligence – IBERAMIA 2008. IBERAMIA 2008. Lecture Notes in Computer Science(), vol 5290. Springer, Berlin, Heidelberg.

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