Locating Anomalies using Bayesian Factorizations and Masks
Abstract
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.
Recommended Citation
L. Yao et al., "Locating Anomalies using Bayesian Factorizations and Masks," ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 207 - 212, European Symposium on Artificial Neural Networks, Dec 2010.
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-287419044-5
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 European Symposium on Artificial Neural Networks, All rights reserved.
Publication Date
01 Dec 2010
