High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …

A Historical Account of Types of Fuzzy Sets and Their Relationships Page 1 IEEE
TRANSACTIONS ON FUZZY SYSTEMS, VOL. 24, NO. 1, FEBRUARY 2016 179 A Historical …

Abstract The k-Nearest Neighbor (k NN) algorithm is widely used in the supervised learning
field and, particularly, in search and classification tasks, owing to its simplicity, competitive …

In the era of big data, analyzing and extracting knowledge from large-scale data sets is a
very interesting and challenging task. The application of standard data mining tools in such …

Abstract Machine learning-based classification techniques provide support for the decision-
making process in many areas of health care, including diagnosis, prognosis, screening, etc …

Data reduction, aiming to reduce the original data by selecting the most representative
information, is an important technique of preprocessing data. At present, large-scale or huge …

Feature selection facilitates uncertainty disposal and information mining, and it has received
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …

Diabetes, a metabolic disease in which the blood glucose level rises over time, is one of the
most common chronic diseases at present. It is critical to accurately predict and classify …

This chapter emphasizes on the role played by rough set theory (RST) within the broad field
of Machine Learning (ML). As a sound data analysis and knowledge discovery paradigm …

While standing as one of the most widely considered and successful supervised
classification algorithms, the k-nearest Neighbor (kNN) classifier generally depicts a poor …