Drug discovery and development is a time-consuming process that involves identifying,
designing, and testing new drugs to address critical medical needs. In recent years, machine …

Effective marine oil spill management (MOSM) is crucial to minimize the catastrophic
impacts of oil spills. MOSM is a complex system affected by various factors, such as …

Data imbalance is frequently encountered in biomedical applications. Resampling
techniques can be used in binary classification to tackle this issue. However such solutions …

Precision-Recall analysis abounds in applications of binary classification where true
negatives do not add value and hence should not affect assessment of the classifier's …

Incomplete data problem is commonly existing in classification tasks with multi-source data,
particularly the disease diagnosis with multi-modality neuroimages, to track which, some …

Many supervised learning problems involve learning from samples whose labels are
corrupted in some way. For example, each sample may have some constant probability of …

Artificial intelligence has revolutionized medical diagnosis, particularly for cancers. Acute
myeloid leukemia (AML) diagnosis is a tedious protocol that is prone to human and machine …

Empirical risk minimization (ERM) is sensitive to spurious correlations present in training
data, which poses a significant risk when deploying systems trained under this paradigm in …

The goal of classification with rejection is to avoid risky misclassification in error-critical
applications such as medical diagnosis and product inspection. In this paper, based on the …

Binary classification is one of the most frequent studies in applied machine learning
problems in various domains, from medicine to biology to meteorology to malware analysis …