Locating anomalies using Bayesian factorizations and masks

Li Yao, Amaury Lendasse, Francesco Corona

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


    A plethora of methods have been developed to handle anomaly detection in various application domains. This work focuses on locating anomalies inside a categorical data set without assuming any specific domain knowledge. By exploiting the conditional dependence and independence relationships among data attributes, not only can data analysts recognize the anomaly, but also locate the potentially anomalous attributes inside an anomalous instance following its masks. Masks are geometrically generated based on the factorization of the joint probability from a Bayesian network automatically learnt from the given data set.

    Original languageEnglish
    Title of host publicationProceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
    Number of pages6
    Publication statusPublished - 1 Dec 2010
    MoE publication typeA4 Article in a conference publication


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