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

An Attribute Reduction Method Based on Rough Set and SVM and with Application in Oil-Gas Prediction

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)
Nie Ru ; China Univ. of Min. & Technol., Xuzhou ; Yue Jianhua

With greater generalization performance support vector machine (SVM) is a new machine learning method. Rough set theory is a new powerful tool h dealing with vagueness and uncertainty information. By combining the advantages of two approaches, an original attribute reduction method is proposed in the paper. Moreover, it is applied into oil-gas prediction to solve the problems when support vector machine is directly employed. Experiments and results show the validity and feasibility of the algorithm suggested in the paper.

Published in:

Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS International Conference on

Date of Conference:

11-13 July 2007

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.