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Traditional machine learning approaches may fail to perform satisfactorilywhen dealing with complex data. In this context, the importance of data miningevolves w.r.t. building an efficient knowledge discovery and mining framework.Ensemble learning is aimed at integration of fusion, modeling and mining ofdata into a unified model. However, traditional ensemble learning methods arecomplex and have optimization or tuning problems. In this paper, we propose asimple, sequential, efficient, ensemble learning approach using multiple deepnetworks. The deep network used in the ensembles is ResNet50. The model drawsinspiration from binary decision/classification trees. The proposed approach iscompared against the baseline viz. the single classifier approach i.e. using asingle multiclass ResNet50 on the ImageNet and Natural Images datasets. Ourapproach outperforms the baseline on all experiments on the ImageNet dataset.Code is available in https://github.com/mueedhafiz1982/CNNTreeEnsemble.git

Deep Network Ensemble Learning applied to Image Classification using CNN Trees