Outlier detection research is currently focusing on the development of new methods and on improving the computation time for these methods. Evaluation however is rather heuristic,...
Erich Schubert, Remigius Wojdanowski, Arthur Zimek...
Projective Clustering Ensembles (PCE) are a very recent advance in data clustering research which combines the two powerful tools of clustering ensembles and projective clustering...
Francesco Gullo, Carlotta Domeniconi, Andrea Tagar...
This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and fin...
Abstract. Ensemble methods allow to improve the accuracy of classification methods. This work considers the application of one of these methods, named Rotation-based, when the clas...
Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a (weighted) vote of their predictions. The original ensembl...
While learning ensembles have been widely used for various pattern recognition tasks, surprisingly, they have found limited application in problems related to medical image analysi...
Anant Madabhushi, Jianbo Shi, Michael D. Feldman, ...
An ensemble is a set of learned models that make decisions collectively. Although an ensemble is usually more accurate than a single learner, existing ensemble methods often tend ...
Abstract. Ensemble methods are able to improve the predictive performance of many base classifiers. Up till now, they have been applied to classifiers that predict a single target ...
Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzerosk...
Abstract. It is well known that diversity among component classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods achieve this goal through resam...
Abstract. Ensemble methods can achieve excellent performance relying on member classifiers’ accuracy and diversity. We conduct an empirical study of the relationship of ensemble...