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- W1537358011 abstract "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.KeywordsActive LearningBayesian NetworkAnomaly DetectionSynthetic DatasetDirichlet DistributionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W1537358011 date "2009-01-12" @default.
- W1537358011 modified "2023-09-26" @default.
- W1537358011 title "Detection of Anomalies in Large Datasets Using an Active Learning Scheme Based on Dirichlet Distributions" @default.
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- W1537358011 doi "https://doi.org/10.1007/978-3-540-88309-8_17" @default.
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