Many systems (manufacturing, environmental, health, etc.) generate counts (or rates) of events that are monitored to detect changes. Modern data complements event counts with many additional measurements (such as geographic, demographic, and others) that comprise high-dimensional attributes. This leads to an important challenge to detect a change that only occurs within a region, initially unspecified, defined by these attributes and current methods to handle the attribute information are challenged by high-dimensional data. Our approach transforms the problem to supervised learning, so that properties of an appropriate learner can be described. Rather than error rates, we generate a signal (of a system change) from an appropriate feature selection algorithm. A measure of statistical significance is included to control false alarms. Results on simulated examples are provided.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2011 - 21st International Conference on Artificial Neural Networks, Proceedings
Number of pages8
EditionPART 2
StatePublished - 2011
Event21st International Conference on Artificial Neural Networks, ICANN 2011 - Espoo, Finland
Duration: Jun 14 2011Jun 17 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6792 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other21st International Conference on Artificial Neural Networks, ICANN 2011


  • Feature selection
  • process control
  • tree ensembles

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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