2020/07/23 by Abdul Mueed Hafiz, Hafiz, Abdul Mueed, G. Mohiuddin Bhat +2
Computer Science · #Advanced Neural Network Applications #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning and Data Classification #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #cs.CV #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.2008.00829
arxiv created 2020/07/23 · openalex publication_date 2020/07/23 · arxiv updated 2020/08/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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