Abstract. Understanding ensemble diversity is one of the most important fundamental issues in ensemble learning. Inspired by a recent work trying to explain ensemble diversity from...
Abstract. Ensemble methods can achieve excellent performance relying on member classifiers’ accuracy and diversity. We conduct an empirical study of the relationship of ensemble...
Clustering ensembles has been recently recognized as an emerging approach to provide more robust solutions to the data clustering problem. Current methods of clustering ensembles ...
In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise th...
Most works based on diversity suggest that there exists only weak correlation between diversity and ensemble accuracy. We show that by combining the diversities with the classifica...
Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souz...