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Learning Coordination Classifiers

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We present a new approach to ensemble classification that requires learning only a single base classifier. The idea is to learn a classifier that simultaneouslypredicts pairs of test labels -- as opposed tolearning multiple predictors for single test labels --then coordinating the assignment of individual labelsby propagating beliefs on a graph over the data.We argue that the approach is statistically well motivated,even for independent identically distributed(iid) data. In fact, we present experimental resultsthat show improvements in classification accuracyover single-example classifiers, across a range ofiid data sets and over a set of base classifiers. Likeboosting, the technique increases representationalcapacity while controlling variance through a principledform of classifier combination.


Y. Guo, R. Greiner, D. Schuurmans. "Learning Coordination Classifiers". International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, Scotland, August 2005.

Keywords: machine learning, ensemble methods
Category: In Conference


  author = {Yuhong Guo and Russ Greiner and Dale Schuurmans},
  title = {Learning Coordination Classifiers},
  booktitle = {International Joint Conference on Artificial Intelligence
  year = 2005,

Last Updated: April 23, 2007
Submitted by Nelson Loyola

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