AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning

1 code implementation • Neurocomputing 2020 • Aboozar Taherkhani, Georgina Cosma, T. M. McGinnity

AdaBoost-CNN is computationally efficient, as evidenced by the fact that the training simulation time of the proposed method is 47. 33 s, which is lower than the training simulation time required for a similar AdaBoost method without transfer learning, i. e. 225. 83 s on the imbalanced dataset.

BIG-bench Machine Learning Transfer Learning