Trading between classification accuracy and information in production

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

There is a tradeoff between the accuracy of a classification model and the amount of information it provides. Increase in the amount of information in a decision often comes at the expense of the decision accuracy. For example, discretization of a continuous target variable such as a production tool work in process (WIP) using more levels increases information but also raises the errors in WIP discretization. An information measure (IM) that trades between the two, using the mutual information between predictions and true decisions, is proposed. The superiority of IM over other performance measures is manifested in various scenarios. In addition, an unsupervised, IM-based discretization strategy is suggested. This strategy determines the number and positions of the discretization splits to increase the amount of information in the discretization while minimizing the error severity. The strategy is applied to the discretization of WIP in a chain of tools of a production FAB.

Original languageEnglish
Title of host publication21st International Conference on Production Research
Subtitle of host publicationInnovation in Product and Production, ICPR 2011 - Conference Proceedings
EditorsTobias Krause, Dieter Spath, Rolf Ilg
PublisherFraunhofer-Verlag
ISBN (Electronic)9783839602935
StatePublished - 1 Jan 2011
Event21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Stuttgart, Germany
Duration: 31 Jul 20114 Aug 2011

Conference

Conference21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011
Country/TerritoryGermany
CityStuttgart
Period31/07/114/08/11

Keywords

  • Classification accuracy
  • Data mining
  • Discretization
  • Information
  • Machine learning
  • Work in process

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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