Diversity regularized ensemble pruning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Bagging ensemble selection for regression
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Embedding change rate estimation based on ensemble learning
Proceedings of the first ACM workshop on Information hiding and multimedia security
Detecting insider threats in a real corporate database of computer usage activity
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic construction of anomaly detection benchmarks from real data
Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description
An empirical study of top-n recommendation for venture finance
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
On the doubt about margin explanation of boosting
Artificial Intelligence
Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air
International Journal of Knowledge Discovery in Bioinformatics
Accurate probability calibration for multiple classifiers
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Learning ensemble classifiers via restricted Boltzmann machines
Pattern Recognition Letters
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
Sentiment classification: The contribution of ensemble learning
Decision Support Systems
A Roadmap for Designing a Personalized Search Tool for Individual Healthcare Providers
Journal of Medical Systems
On a method for constructing ensembles of regression models
Automation and Remote Control
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An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.