In a majority–minority classification problem, class imbalance in the dataset (s) can
dramatically skew the performance of classifiers, introducing a prediction bias for the …

This study reviews the common practices and procedures conducted to identify the cost
drivers that the past literature has classified into two main categories: qualitative and …

The term" fuzzy logic," as it is understood in this book, stands for all aspects of representing
and manipulating knowledge based on the rejection of the most fundamental principle of …

Zero-shot recognition (ZSR) deals with the problem of predicting class labels for target
domain instances based on source domain side information (eg attributes) of unseen …

The class imbalance problems have been reported to severely hinder classification
performance of many standard learning algorithms, and have attracted a great deal of …

In recent years, a great number of publications have explored the use of genetic algorithms
as a tool for designing fuzzy systems. Genetic Fuzzy Systems explores and discusses this …

This paper introduces a novel fuzzy rule-based classification method called FURIA, which is
short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the well-known RIPPER …

Image binarization is the process of separation of pixel values into two groups, white as
background and black as foreground. Thresholding plays a major in binarization of images …

Classification with big data has become one of the latest trends when talking about learning
from the available information. The data growth in the last years has rocketed the interest in …

Although fuzzy systems demonstrated their ability to solve different kinds of problems in
various applications, there is an increasing interest on augmenting them with learning …