By Topic

A New Diverse Measure in Ensemble Learning Using Unlabeled Data

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
$33 $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

4 Author(s)
Rong Chu ; Coll. of Comput. & Inf., Hohai Univ. Nanjing, Nanjing, China ; Min Wang ; Xiaoqin Zeng ; Lixin Han

Ensemble learning has been successfully used in many areas, due to its powerful ability to solve complex problems. In recent years, some researchers have shown that ensemble of some learners instead of all individual learners could get better performances. However, how to select individual learners as diverse as possible is a very important issue. In this paper, a new diversity measure is proposed to achieve a better selection of individual learners. Different from the commonly used diversity measures, it makes full of the data distribution information provided by the cheap and abundant unlabeled data rather than the expensive and scarce labeled data in order to obtain the higher classification accuracy. The selection method based on the new diversity measure is simple in computation and independent of models. Experimental results demonstrate its good performances.

Published in:

Computational Intelligence, Communication Systems and Networks (CICSyN), 2012 Fourth International Conference on

Date of Conference:

24-26 July 2012