Class Imbalance Problem.

TL;DR: Simulation data from a high fidelity Finite-Discrete Element Model is used to build an efficient Machine Learning (ML) approach to predict fracture growth and coalescence and achieves a reduction in computational cost by multiple orders of magnitude.

TL;DR: In this paper , the authors review the health-equity implications of applying AI to mental health problems, outline state-of-the-art methods for assessing and mitigating algorithmic bias, and present a call to action to guide the development of fair-aware AI in psychological science.

TL;DR: In this article , a hybridization of Generative Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) is proposed to address class imbalanced problems.

TL;DR: An original machine learning approach to assess and monitor the poverty status of Jordanian households is proposed, which takes into account all the household expenditure and income surveys that took place since the early beginning of the new millennium and makes poverty identification cheaper and much closer to real-time.

TL;DR: This paper argues that severe class imbalance is not just an interesting technical challenge that improved learning algorithms will address, it is much more serious.