GENE CANCER CLASSIFICATION USING ENHANCED TRANSFERABLE REINFORCEMENT LEARNING AND ENHANCED RESNET ALGORITHM

In this paper, we propose a novel approach to cancer classification using microarray datasets which integrates transfer learning, improved feature selection technique and state of the art Deep Learning (DL) models. Traditional machine learning algorithms usually have high computational costs when dealing with high dimensional, highly noisy microarray datasets with a large number of irrelevant or redundant features.