Gait is the locomotion attained through the movement of limbs and gait analysis examines
the patterns (normal/abnormal) depending on the gait cycle. It contributes to the …

Node classification is an important research topic in graph learning. Graph neural networks
(GNNs) have achieved state-of-the-art performance of node classification. However, existing …

Several performance metrics are currently available to evaluate the performance of Machine
Learning (ML) models in classification problems. ML models are usually assessed using a …

Binary classifiers are often required to possess fairness in the sense of not overly
discriminating with respect to a feature deemed sensitive eg race. We study the inherent …

Multi-label classification problems usually occur in tasks related to information retrieval, like
text and image annotation, and are receiving increasing attention from the machine learning …

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 …

Many binary class datasets in real-life applications are affected by class imbalance problem.
Data complexities like noise examples, class overlap and small disjuncts problems are …

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 …

Data imbalance, ie, some classes may have much fewer samples than others, is a serious
problem that can lead to unfavorable node classification. However, most existing GNNs are …

Average precision (AP), the area under the recall-precision (RP) curve, is the standard
performance measure for object detection. Despite its wide acceptance, it has a number of …