A Novel Combining-Based Method of Pool Generation for Ensemble Regression Problems

A crucial point for ensemble learning systems is the capacity of making different errors on any given sample, which highlights the importance of diversity for ensemble-based decision systems. A usual way of increasing diversity is to combine traditional ensemble methods. Based on this context, we propose a novel combining-based algorithm of pool generation using a merging of bagging, random patches, and boosting techniques for ensemble regression problems. Numerical results indicate that, depending on both the dataset and the diversity measurement, our proposal generates a pool of regressors with more diversity when compared to single ensemble generator approaches.