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

k-NN Numeric Prediction Using Bagging and Instance-relevant Combination

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)
Liang He ; Dept. of Comput. Sci. & Technol., Xi''an Jiaotong Univ., Xi''an, China ; Qinbao Song ; Junyi Shen

The fixed number of reference neighbors leads to mutable prediction errors and low overall accuracy for various unknown instances, according to existing k-NN methods. To address this problem, a bagging-based k-NN numeric prediction algorithm with attribute selection is proposed. Within each training procedure, a set of base k-NN predictors are built iteratively in terms of different bootstrap sampling datasets. Then, the base predictors estimate the unknown instance respectively. The combination mechanism of these individual outcomes determines the performance of this ensemble algorithm. Hence, an instance-relevant rule is proposed to calculate the composite result. The weight of each base k-NN predictor is dynamically updated with respect to the distinct features of current unknown instance. The accuracy in response to different number of base k-NN predictors is also explored. The experimental results on public datasets show the considerable improvement of k-NN numeric prediction.

Published in:

Data, Privacy and E-Commerce (ISDPE), 2010 Second International Symposium on

Date of Conference:

13-14 Sept. 2010

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.