Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Random Sampling for Subspace Face Recognition
International Journal of Computer Vision
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Nonlinear Boosting Projections for Ensemble Construction
The Journal of Machine Learning Research
BoostCluster: boosting clustering by pairwise constraints
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Ensembling local learners ThroughMultimodal perturbation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bagging Constraint Score for feature selection with pairwise constraints
Pattern Recognition
Semisupervised kernel matrix learning by kernel propagation
IEEE Transactions on Neural Networks
Ensemble pruning via base-classifier replacement
WAIM'11 Proceedings of the 12th international conference on Web-age information management
A new metric for greedy ensemble pruning
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Distance-based feature selection on classification of uncertain objects
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Energy-Based metric for ensemble selection
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Biclustering-driven ensemble of Bayesian belief network classifiers for underdetermined problems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Constraint projections for semi-supervised affinity propagation
Knowledge-Based Systems
Multi-label ensemble based on variable pairwise constraint projection
Information Sciences: an International Journal
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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.