In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …

Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

Clinical decision-making in healthcare is already being influenced by predictions or
recommendations made by data-driven machines. Numerous machine learning applications …

In clinical settings, a lot of medical image datasets suffer from the imbalance problem which
hampers the detection of outliers (rare health care events), as most classification methods …

Increasingly, interventions aimed at improving care are likely to use such technologies as
machine learning and artificial intelligence. However, health care has been relatively late to …

The majority of patients in the hospital are ambulatory and would benefit significantly from
predictive and personalized monitoring systems. Such patients are well suited to having …

Abstract ASHRAE Global Thermal Comfort Database has been extensively used for
analyzing specific thermal comfort parameters or models, evaluating subjective metrics, and …

In the emerging field of acoustic novelty detection, most research efforts are devoted to
probabilistic approaches such as mixture models or state‐space models. Only recent studies …

Advances in wearable sensing and communications infrastructure have allowed the
widespread development of prototype medical devices for patient monitoring. However …

Early diagnosis of critically ill patients depends on the attention and observation of medical
staff about different variables, as vital signs, results of laboratory tests, among other …