Anomaly detection in data streams requires a signal of an unusual event, but an actionable response requires diagnostics. Consequently, an important task is to isolate to the few key attributes that contribute to the signal from among a large collection. We introduce this contributor problem to the machine learning community and present a solution for monitoring in modern systems (with nonlinear reference conditions, high dimensions, categorical attributes, missing data, and so forth). The objective is to identify attributes that contribute to a signal, for both individual and multiple anomalies, or from several anomaly groups. Although related to the feature selection problem, the extreme sparseness of anomalies leads to scores that are designed specifically for the contributors problem. Statistical criteria are provided to quantitatively address decision rules and false alarms and the method can be computed quickly. Comparisons are made to traditional contribution plots.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings
Number of pages10
EditionPART 2
StatePublished - Nov 27 2009
Event19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol, Cyprus
Duration: Sep 14 2009Sep 17 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5769 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other19th International Conference on Artificial Neural Networks, ICANN 2009

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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