Constraint Projections for Ensemble Learning

Daoqiang Zhang, Songcan Chen, Zhi-Hua Zhou, Qiang Yang

It is well-known that diversity among base classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods obtain diverse individual learners through resampling the instances or features. In this paper, we propose an alternative way for ensemble construction by resampling pairwise constraints that specify whether a pair of instances belongs to the same class or not. Using pairwise constraints for ensemble construction is challenging because it remains unknown how to influence the base classifiers with the sampled pairwise constraints. We solve this problem with a two-step process. First, we transform the original instances into a new data representation using projections learnt from pairwise constraints. Then, we build the base classifiers with the new data representation. We propose two methods for resampling pairwise constraints following the standard Bagging and Boosting algorithms, respectively. Extensive experiments validate the effectiveness of our method.

Subjects: 12. Machine Learning and Discovery; Please choose a second document classification

Submitted: Apr 15, 2008

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