Exploring the Potential of Feature Density in Estimating Machine Learning Classifier Performance with Application to Cyberbullying Detection. (arXiv:2206.01949v1 [cs.CL])

In this research. we analyze the potential of Feature Density (HD) as a way
to comparatively estimate machine learning (ML) classifier performance prior to
training. The goal of the study is to aid in solving the problem of
resource-intensive training of ML models which is becoming a serious issue due
to continuously increasing dataset sizes and the ever rising popularity of Deep
Neural Networks (DNN). The issue of constantly increasing demands for more
powerful computational resources is also affecting the …

application arxiv cyberbullying detection feature learning machine machine learning performance