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

A similarity evaluation technique for data mining with an ensemble of classifiers

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)
Punronen, S. ; Jyvaskyla Univ., Finland ; Terziyan, V.

Evaluation of similarity is very important in data mining with an ensemble of classifiers. Similarity between instances is used to recognize the nearest neighbors of an instance, similarity between classes is necessary to derive the misclassification errors in the learning phase, and similarity between classifiers is used to evaluate the classifiers when they are integrated. In the similarity evaluation we use a training set consisting predicates that define relationships within the three sets: the set of instances, the set of classes, and the set of classifiers. We consider two ways to derive similarities

Published in:

Database and Expert Systems Applications, 2000. Proceedings. 11th International Workshop on

Date of Conference:

2000

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.