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

Authors: Prasun Das, Sandip Mukherjee

Addresses: SQC & OR Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata – 700 108, India. ' SQC & OR Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata – 700 108, India

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.

DOI: 10.1504/IJISE.2009.026770

International Journal of Industrial and Systems Engineering, 2009 Vol.4 No.6, pp.665 - 682

Published online: 26 Jun 2009 *

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