Although diversity in classifier ensembles is desirable, its relationship with the ensemble accuracy is not straightforward. Here we derive a decomposition of the majority vote er...
— This paper proposes to incorporate bootstrap of data, random feature subspace and evolutionary algorithm with negative correlation learning to automatically design accurate and...
A new method for ensemble generation is presented. It is based on grouping the attributes in dierent subgroups, and to apply, for each group, an axis rotation, using Principal Com...
Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how...
Abstract. Ensemble selection deals with the reduction of an ensemble of predictive models in order to improve its efficiency and predictive performance. A number of ensemble select...
Ioannis Partalas, Grigorios Tsoumakas, Ioannis P. ...