In recent years, many nearest neighbor algorithms based on fuzzy sets theory have been
developed. These methods form a field, known as fuzzy nearest neighbor classification …

Data used in machine learning applications is prone to contain both vague and incomplete
information. Many authors have proposed to use fuzzy rough set theory in the development …

This book is based on my Ph. D. dissertation completed at Ghent University (Belgium) and
the University of Granada (Spain) in June 2018. It focuses on classification. The goal is to …

The k Nearest Neighbour (kNN) method is one of the most popular algorithm in clustering
and data classification. The kNN algorithm founds to be performed very efficient in the …

En el presente trabajo se realiza una revisión bibliográfica de la gestión del mantenimiento
enfocado a la confiabilidad ya las fallas en interruptores de potencia. Se valoran los …

Fuzzy sets and rough sets are known as uncertainty models. They are proposed to treat
different aspects of uncertainty. Therefore, it is natural to combine them to build more …

In 2006, Clive Humby said at the ANA Senior marketer's summit that Data is the new oil and
this might not be far from the truth. In its raw format oil is not valuable, but once it is refined …

Anomaly detection is a significant area of discovering knowledge that has shown success in
the areas of fraud detection, cyber security, and medical diagnostics. The kernelized fuzzy …

The purpose of the intrusion detection systems is to detect attacks on computer systems and
networks. Many technologies can be used for intrusion detection, and one of the most …

Mammographic risk analysis is an important task for assessing the likelihood of a woman
developing breast cancer. It has attracted much attention in recent years as it can be used as …