By Topic

Evaluation of Feature Extraction Methods on Software Cost Estimation

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

3 Author(s)
Turhan, B. ; Bogazici Univ., Istanbul ; Kutlubay, O. ; Bener, A.

This research investigates the effects of linear and non-linear feature extraction methods on the cost estimation performance. We use principal component analysis (PCA) and Isomap for extracting new features from observed ones and evaluate these methods with support vector regression (SVR) on publicly available datasets. Our results for these datasets indicate there is no significant difference between the performances of these linear and non-linear feature extraction methods.

Published in:

Empirical Software Engineering and Measurement, 2007. ESEM 2007. First International Symposium on

Date of Conference:

20-21 Sept. 2007