Technical Briefs

Data-Dimensionality Reduction Using Information-Theoretic Stepwise Feature Selector

[+] Author and Article Information
Alok A. Joshi1

 Cummins Inc., 1900 McKinley Avenue, MC 50174, Columbus, IN 47201alok.a.joshi@cummins.com

Peter Meckl, Galen King

Ray W. Herrick Laboratories, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907-2031

Kristofer Jennings

Department of Statistics, College of Science, Purdue University, West Lafayette, IN 47907-2067


Corresponding author.

J. Dyn. Sys., Meas., Control 131(4), 044503 (May 19, 2009) (5 pages) doi:10.1115/1.3023112 History: Received June 26, 2007; Revised August 25, 2008; Published May 19, 2009

A novel information-theoretic stepwise feature selector (ITSFS) is designed to reduce the dimension of diesel engine data. This data consist of 43 sensor measurements acquired from diesel engines that are either in a healthy state or in one of seven different fault states. Using ITSFS, the minimum number of sensors from a pool of 43 sensors is selected so that eight states of the engine can be classified with reasonable accuracy. Various classifiers are trained and tested for fault classification accuracy using the field data before and after dimension reduction by ITSFS. The process of dimension reduction and classification is repeated using other existing dimension reduction techniques such as simulated annealing and regression subset selection. The classification accuracies from these techniques are compared with those obtained by data reduced by the proposed feature selector.

Copyright © 2009 by American Society of Mechanical Engineers
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Grahic Jump Location
Figure 1

Information-theoretic stepwise feature selector—flowchart

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Figure 2

Each subfigure is a plot of percentage weighted accuracy in classification on the Y-axis for various classifiers represented by the legend. Various feature selection methods are shown on the X-axis along with the full input space. (a) Full and 21-dimensional feature space; (b) full and 8-dimensional feature space.




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