An unsupervised classification scheme for multi-class problems including feature selection based on MTS philosophy

Prasun Das and Sandip Mukherjee

International Journal of Industrial and Systems Engineering, 2009, vol. 4, issue 6, 665-682

Abstract: In this paper, the proposed Unsupervised Mahalanobis Distance Classifier (UNMDC) scheme is a multi-class unsupervised classifier with the basic philosophy of supervised Mahalanobis–Taguchi System (MTS) based monitoring procedure. A comparative study between the MTS and the proposed UNMDC is performed with various simulated experiments for different types of correlation structure and location parameters, published data and real-life data sets of different sizes and dimensions. The advantages of domain knowledge independent thresholds, multi-class separation, identifying process shifts during multivariate process-monitoring and feature selection in case of detection of abnormals are the special merits of this algorithm.

Keywords: unsupervised learning; Mahalanobis distance; MTS philosophy; feature selection; threshold; severity level; Mahalanobis–Taguchi system; process monitoring. (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:4:y:2009:i:6:p:665-682

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