Local Negative Correlation with Resampling.

This paper deals with a learning algorithm which combines two well known methods to generate ensemble diversity - error negative correlation and resampling. In this algorithm, a set of learners iteratively and synchronously improve their state considering information about the performance of a fixed number of other learners in the ensemble, to generate a sort of local negative correlation. Resampling allows the base algorithm to control the impact of highly influential data points which in turns can improve its generalization error. The resulting algorithm can be viewed as a generalization of bagging, where each learner no longer is independent but can be locally coupled with other learners. We will demonstrate our technique on two real data sets using neural networks ensembles. [ABSTRACT FROM AUTHOR]