Feature Selection for mixed data is an active research area with many applications in
practical problems where numerical and non-numerical features describe the objects of …

Attribute reduction, also called feature selection, is one of the most important issues of rough
set theory, which is regarded as a vital preprocessing step in pattern recognition, machine …

This study proposes an alternate data extraction method that combines three well-known
feature selection methods for handling large and problematic datasets: the correlation …

Due to limitations in data acquisition, data in real life often contains a wealth of uncertain
information. Uncertainty measurement (UM) constructed within the framework of rough set …

Data scenarios on nowadays comprise an enormous number of attributes and instances
while not all attributes are necessary and useful for data analytics and knowledge extraction …

Healthcare is a big concern in the current booming population. Many approaches for
improving health are imposed, such as early disease identification, treatment, and …

Abstract In recent years, Unsupervised Feature Selection (UFS) methods have attracted
considerable interest in different research areas due to their wide application in problems …

Chronic kidney disease (CKD) is a major public health concern with rising prevalence and
huge costs associated with dialysis and transplantation. Early prediction of CKD can reduce …

Label distribution learning (LDL) is an emerging framework in machine learning. Fuzzy
mutual information is mutual information under a fuzzy environment and plays an important …

This paper addresses the current existence of attribute reduction algorithms for incomplete
hybrid decision-making systems, including low attribute reduction efficiency, low …