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The relative performance of ensemble methods with deep convolutional neural networks for image classification

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  • Cheng Ju
  • Aurélien Bibaut
  • Mark van der Laan

Abstract

Artificial neural networks have been successfully applied to a variety of machine learning tasks, including image recognition, semantic segmentation, and machine translation. However, few studies fully investigated ensembles of artificial neural networks. In this work, we investigated multiple widely used ensemble methods, including unweighted averaging, majority voting, the Bayes Optimal Classifier, and the (discrete) Super Learner, for image recognition tasks, with deep neural networks as candidate algorithms. We designed several experiments, with the candidate algorithms being the same network structure with different model checkpoints within a single training process, networks with same structure but trained multiple times stochastically, and networks with different structure. In addition, we further studied the overconfidence phenomenon of the neural networks, as well as its impact on the ensemble methods. Across all of our experiments, the Super Learner achieved best performance among all the ensemble methods in this study.

Suggested Citation

  • Cheng Ju & Aurélien Bibaut & Mark van der Laan, 2018. "The relative performance of ensemble methods with deep convolutional neural networks for image classification," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(15), pages 2800-2818, November.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:15:p:2800-2818
    DOI: 10.1080/02664763.2018.1441383
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    Cited by:

    1. Hyungjin Ko & Jaewook Lee & Junyoung Byun & Bumho Son & Saerom Park, 2019. "Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis," Sustainability, MDPI, vol. 11(12), pages 1-24, June.
    2. Doyun Kim & Joowon Chung & Jongmun Choi & Marc D. Succi & John Conklin & Maria Gabriela Figueiro Longo & Jeanne B. Ackman & Brent P. Little & Milena Petranovic & Mannudeep K. Kalra & Michael H. Lev & , 2022. "Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    3. Iftikhar Ahmad & Abdul Qayyum & Brij B. Gupta & Madini O. Alassafi & Rayed A. AlGhamdi, 2022. "Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI," Mathematics, MDPI, vol. 10(4), pages 1-23, February.
    4. Mireia Crispin-Ortuzar & Ramona Woitek & Marika A. V. Reinius & Elizabeth Moore & Lucian Beer & Vlad Bura & Leonardo Rundo & Cathal McCague & Stephan Ursprung & Lorena Escudero Sanchez & Paula Martin-, 2023. "Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. JoonBum Leem & Ha Young Kim, 2020. "Action-specialized expert ensemble trading system with extended discrete action space using deep reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-39, July.
    6. Prakhar Bansal & Rahul Kumar & Somesh Kumar, 2021. "Disease Detection in Apple Leaves Using Deep Convolutional Neural Network," Agriculture, MDPI, vol. 11(7), pages 1-23, June.

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