Diagnosis, being the first step in medical practice, is very crucial for clinical decision making.
This paper investigates state-of-the-art computational intelligence (CI) techniques applied in …

In real world data are often plagued by missing values which adversely affects the final
outcome of the analysis based on such data. The missing values can be handled using …

Background Advances in medical technology have allowed for customized prognosis,
diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple …

Missing values are common in the Internet of Things (IoT) environment for various reasons,
including regular maintenance or malfunction. In time-series prediction in the IoT, missing …

Background To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and
associated co-morbidities, a better understanding of the pathophysiology and risk factors for …

The missing data issue is a fundamental challenge in terms of analyses and classification of
data. The classification performance of incomplete data could be affected and produce …

The presence of missing components in incomplete instances precludes a kernel-based
model from incorporating partially observed components of incomplete instances and …

In semiconductor manufacturing, virtual metrology (VM) is a method of predicting physical
measurements of wafer qualities using in-process information from sensors on production …

Incomplete data are often neglected when designing machine learning methods. A popular
strategy adopted by practitioners to circumvent this consists of taking a preprocessing step to …

Every classification model contains uncertainty. This uncertainty can be distributed evenly or
into certain areas of feature space. In regular classification tasks, the uncertainty can be …