Cart (Loading....) | Create Account
Close category search window
 

A weight analysis-based wrapper approach to neural nets feature subset selection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Schuschel, D. ; Longbow Apache Software, Boeing Co., Mesa, AZ, USA ; Chun-Nan Hsu

This paper presents a novel attribute selection approach for backprop neural networks. Previously, an attribute selection technique known as the wrapper model was shown effective for decision tree induction. However, it is prohibitively expensive when applied to real-world neural net training characterized by large volumes of data and many attribute choices. Our approach incorporates a weight analysis based heuristic called ANNIGMA to direct the search in the wrapper model and allows effective attribute selection feasible for neural net applications. Experimental results on standard data sets show that this approach can efficiently reduce the number of inputs while maintaining or even improving the accuracy. We also report two successful applications of our approach in the helicopter maintenance applications

Published in:

Tools with Artificial Intelligence, 1998. Proceedings. Tenth IEEE International Conference on

Date of Conference:

10-12 Nov 1998

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.