Diversity and Generalization in Neural Network Ensembles

Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa

Research output: Contribution to journalConference article in JournalResearchpeer-review

6 Citations (Scopus)


Ensembles are widely used in machine learning and, usually, provide state-of-the-art performance in many prediction tasks. From the very beginning, the diversity of an ensemble has been identified as a key factor for the superior performance of these models. But the exact role that diversity plays in ensemble models is poorly understood, specially in the context of neural networks. In this work, we combine and expand previously published results in a theoretically sound framework that describes the relationship between diversity and ensemble performance for a wide range of ensemble methods. More precisely, we provide sound answers to the following questions: how to measure diversity, how diversity relates to the generalization error of an ensemble, and how diversity is promoted by neural network ensemble algorithms. This analysis covers three widely used loss functions, namely, the squared loss, the cross-entropy loss, and the 0-1 loss; and two widely used model combination strategies, namely, model averaging and weighted majority vote. We empirically validate this theoretical analysis with neural network ensembles.

Original languageEnglish
Book seriesProceedings of Machine Learning Research
Pages (from-to)11720-11743
Number of pages24
Publication statusPublished - 2022
Event25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022 - Virtual, Online, Spain
Duration: 28 Mar 202230 Mar 2022


Conference25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022
CityVirtual, Online

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