Abstract

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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages245-252
Number of pages8
Volume6792 LNCS
EditionPART 2
DOIs
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)03029743
ISSN (Electronic)16113349

Other

Other21st International Conference on Artificial Neural Networks, ICANN 2011
CountryFinland
CityEspoo
Period6/14/116/17/11

Fingerprint

Supervised learning
Surveillance
Feature extraction
High-dimensional
Attribute
Health
Count
Statistical Significance
False Alarm
Supervised Learning
High-dimensional Data
Feature Selection
Error Rate
Complement
Transform

Keywords

  • Feature selection
  • process control
  • tree ensembles

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Dávila, S., Runger, G., & Tuv, E. (2011). High-dimensional surveillance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 6792 LNCS, pp. 245-252). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6792 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-21738-8_32

High-dimensional surveillance. / Dávila, Saylisse; Runger, George; Tuv, Eugene.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6792 LNCS PART 2. ed. 2011. p. 245-252 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6792 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Dávila, S, Runger, G & Tuv, E 2011, High-dimensional surveillance. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 6792 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6792 LNCS, pp. 245-252, 21st International Conference on Artificial Neural Networks, ICANN 2011, Espoo, Finland, 6/14/11. https://doi.org/10.1007/978-3-642-21738-8_32
Dávila S, Runger G, Tuv E. High-dimensional surveillance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 6792 LNCS. 2011. p. 245-252. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-21738-8_32
Dávila, Saylisse ; Runger, George ; Tuv, Eugene. / High-dimensional surveillance. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6792 LNCS PART 2. ed. 2011. pp. 245-252 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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