- LSCP: Locally Selective Combination in Parallel Outlier Ensembles
- LightGBM: A Highly Efficient Gradient Boosting Decision Tree
- CatBoost: unbiased boosting with categorical features
- XGBoost: A Scalable Tree Boosting System
- Outlier Detection with Autoencoder Ensembles
- Bagging, Boosting, and C4.5
- Bagging Predictors
- Stacked Regressions
- A decision-theoretic generalization of on-line learning and an application to boosting
- Isolation Forest
- Clusterer Ensemble
- Popular Ensemble Methods: An Empirical Study
- Cluster Ensembles − A Knowledge Reuse Framework for Combining Multiple Partitions
- Ensembles for Unsupervised Outlier Detection: Challenges and Research Questions