Classification Application of Mahalanobis-Taguchi System for Small Datasets

Abstract

The Mahalanobis-Taguchi System (MTS) is a diagnosis and forecasting method for multivariate data that uses the Mahalanobis distance (MD) as a scale to measure a system's performance. MD is based on correlations between a system's variables and the different patterns that can be identified and analyzed with respect to a reference group, referred to as the normal group. MTS is of interest because of its reported accuracy in forecasting small, correlated data sets. the goal of this research is to examine the ability of MTS in regards to differing sizes of normal groups. a probabilistic threshold method (PTM) will be used as a standard method of threshold calculation. the study uses the Wisconsin breast cancer data set with nine factors and two classes, benign and malignant.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Classification; Data mining; Mahalanobis distance (MD); Mahalanobis-Taguchi System (MTS); Multivariate data

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 202 Scimagp Journal and Country Rank, All rights reserved.

Publication Date

01 Jan 2011

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