GENE CANCER CLASSIFICATION USING ENHANCED TRANSFERABLE REINFORCEMENT LEARNING AND ENHANCED RESNET ALGORITHM
DOI:
https://doi.org/10.48047/yz5b5x66Keywords:
Deep Learning, Gene cancer, Reinforcement Learning, ResNet, standard scalar.Abstract
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.
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