Learning patterns through artificial contrasts with application to process control

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

2 Scopus citations

Abstract

In manufacturing as well as other application areas there is a need to learn standard operating conditions in order to detect future changes or deviations. This is related to the even more general problem of detecting instances (cases, records) that are unusual compared to the bulk of the data (outliers). Examples of the problem are fault detection in chemical engineering and statistical process control. The outlier problem is ubiquitous. If specific deviations are not a priori specified, this is a type of unsupervised learning problem. The focus here is on the important, practical case for modern data environments. That is, training data with multiple (usual many) variables of mixed types (without the expedient assumptions common in statistics of multivariate normality that rarely holds in practice). An elegant technique is used to transform an unsupervised learning problem to a supervised one. This methodology uses an artificial reference distribution. For the focus here such a specific reference distribution requires appropriate properties. Then an effective, universal, and nonparametric supervised learner (a gradient boosting machine) is applied to the transformed problem. The results are then in a sense inverted to the original problem. Extensions are mentioned as well as additional insight that becomes available. An illustrative example is presented to justify the validity of this generic and general methodology.

Original languageEnglish (US)
Title of host publicationFourth International Conference on Data Mining, Data Mining IV
EditorsN.F.F.E. Ebecken, C.A. Brebbia, A. Zanasi
PublisherWITPress
Pages63-72
Number of pages10
Volume7
ISBN (Print)1853128309
StatePublished - Dec 1 2003
EventFourth International Conference on Data Mining, Data Mining IV - Rio De Janeiro, Brazil
Duration: Dec 1 2003Dec 3 2003

Other

OtherFourth International Conference on Data Mining, Data Mining IV
CountryBrazil
CityRio De Janeiro
Period12/1/0312/3/03

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Engineering(all)
  • Computer Science Applications
  • Information Systems and Management

Fingerprint Dive into the research topics of 'Learning patterns through artificial contrasts with application to process control'. Together they form a unique fingerprint.

  • Cite this

    Tuv, E., & Runger, G. (2003). Learning patterns through artificial contrasts with application to process control. In N. F. F. E. Ebecken, C. A. Brebbia, & A. Zanasi (Eds.), Fourth International Conference on Data Mining, Data Mining IV (Vol. 7, pp. 63-72). WITPress.