Abstract:
Diverse ensembles are widely used in machine learning and credited with their success. Most of these methods increase ensemble diversity by sampling data or changing model structures. However, a family of ensemble models explicitly promotes diversity in individual error functions. This group’s most famous algorithm is negative correlation learning (NCL) ensemble framework. This article shows that NCL minimizes the ensemble’s errors rather than its residuals. Global negative correlation learning (GNCL), a new ensemble framework, optimizes the global ensemble rather than its components. Under the assumption of fixed basis functions, the NCL framework and global error function provide an analytical solution for base regressor parameters. The general framework can also be instantiated for neural networks with nonfixed basis functions. Extensive regression and classification experiments evaluate the ensemble framework. GNCL outperforms other ensemble methods.
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