Multicriteria variable selection for classification of production batches

Michel J. Anzanello, Susan L. Albin, Wanpracha A. Chaovalitwongse

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

In many industrial processes hundreds of noisy and correlated process variables are collected for monitoring and control purposes. The goal is often to correctly classify production batches into classes, such as good or failed, based on the process variables. We propose a method for selecting the best process variables for classification of process batches using multiple criteria including classification performance measures (i.e.; sensitivity and specificity) and the measurement cost. The method applies Partial Least Squares (PLS) regression on the training set to derive an importance index for each variable. Then an iterative classification/elimination procedure using k-Nearest Neighbor is carried out. Finally, Pareto analysis is used to select the best set of variables and avoid excessive retention of variables. The method proposed here consistently selects process variables important for classification, regardless of the batches included in the training data. Further, we demonstrate the advantages of the proposed method using six industrial datasets.

Original languageEnglish (US)
Pages (from-to)97-105
Number of pages9
JournalEuropean Journal of Operational Research
Volume218
Issue number1
DOIs
StatePublished - Apr 1 2012

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research

Keywords

  • Batch manufacturing
  • Data mining
  • Multiple criteria
  • Multivariate statistics
  • Variable selection

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