# Deep Network Ensemble Learning applied to Image Classification using CNN Trees Source: [Deep Network Ensemble Learning applied to Image Classification using CNN Trees]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:29:00.055Z Authors: Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat Published: 2020-07-23T07:58:25.000Z ## Abstract Traditional machine learning approaches may fail to perform satisfactorily when dealing with complex data. In this context, the importance of data mining evolves w.r.t. building an efficient knowledge discovery and mining framework. Ensemble learning is aimed at integration of fusion, modeling and mining of data into a unified model. However, traditional ensemble learning methods are complex and have optimization or tuning problems. In this paper, we propose a simple, sequential, efficient, ensemble learning approach using multiple deep networks. The deep network used in the ensembles is ResNet50. The model draws inspiration from binary decision/classification trees. The proposed approach is compared against the baseline viz. the single classifier approach i.e. using a single multiclass ResNet50 on the ImageNet and Natural Images datasets. Our approach outperforms the baseline on all experiments on the ImageNet dataset. Code is available in https://github.com/mueedhafiz1982/CNNTreeEnsemble.git ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()