By Topic

Constraint-Based, Transductive Learning for Distributed Ensemble Classification

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)
Miller, D.J. ; Dept. of EE, Penn State Univ., University Park, PA ; Pal, S. ; Yue Wang

We consider ensemble classification when there is no common labeled data for designing the function which aggregates classifier decisions. In recent work, we dubbed this problem distributed ensemble classification, addressing e.g. when local classifiers are trained on different (e.g. proprietary, legacy) databases or operate on different sensing modalities. Typically, fixed (untrained) rules of classifier combination such as voting methods are used in this case. However, these may perform poorly, especially when the local class priors, used in training, differ from the true (test batch) priors. Alternatively, we proposed a transductive strategy, optimizing the combining rule for an objective function measured on the test batch. We proposed both maximum likelihood (ML) and information-theoretic (IT) objectives and found that IT achieved superior performance. Here, we identify that the fundamental advantage of the IT method is its ability to properly account for statistical redundancy in the ensemble. We also develop an extension of IT that improves its performance. Experiments are conducted on the UC Irvine machine learning repository.

Published in:

Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on

Date of Conference:

6-8 Sept. 2006