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Author Babbar, S. ♦ Chawla, S.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2012
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Bayes methods ♦ Mathematical model ♦ Equations ♦ Data mining ♦ Training data ♦ Standards ♦ Educational institutions ♦ Causality and Outliers ♦ Bayesian networks
Abstract Outliers are often identified as data points which are "rare'', "isolated'', or far away from their nearest neighbours. In this paper we demonstrate that meaningful outliers, i.e., outliers which perhaps encode important or new information are those which violate causal relationships. We first build a Bayesian network which encode causal relationships between attributes and then identify those points as outliers which violate these causal relationships. Experiments on several data sets confirm that the outliers identified in this fashion are in some sense "genuine'' as they reveal new information about the underlying data generating process.
Description Author affiliation: Sch. of Inf. Technol., Univ. of Sydney, Sydney, NSW, Australia (Babbar, S.; Chawla, S.)
ISBN 9781479902279
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2012-11-07
Publisher Place Greece
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 409.51 kB
Page Count 8
Starting Page 97
Ending Page 104

Source: IEEE Xplore Digital Library