Precision medicine uses genetic, environmental and lifestyle factors to more accurately
diagnose and treat disease in specific groups of patients, and it is considered one of the …

Despite over two decades of progress, imbalanced data is still considered a significant
challenge for contemporary machine learning models. Modern advances in deep learning …

Purpose This work aims to assess standard evaluation practices used by the research
community for evaluating medical imaging classifiers, with a specific focus on the …

Social networks are an essential component of the Internet of People (IoP) and play an
important role in stimulating interactive communication among people. Graph convolutional …

Finding candidate molecules with favorable pharmacological activity, low toxicity, and
proper pharmacokinetic properties is an important task in drug discovery. Deep neural …

The accuracy of a classification is fundamental to its interpretation, use and ultimately
decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the …

Class imbalance is a prevalent phenomenon in various real-world applications and it
presents significant challenges to model learning, including deep learning. In this work, we …

The class-imbalanced classification is a difficult problem because not only traditional
classifiers are more biased towards the majority classes and inclined to generate incorrect …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

The skewed class distributions of many class imbalanced domain datasets often make it
difficult for machine learning techniques to construct effective models. In such cases, data re …