1 research outputs found
Deep Network Ensemble Learning applied to Image Classification using CNN Trees
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.gi