This book presents novel classification algorithms for four challenging
prediction tasks, namely learning from imbalanced, semi-supervised,
multi-instance and multi-label data. The methods are based on fuzzy
rough set theory, a mathematical framework used to model uncertainty in
data. The book makes two main contributions: helping readers gain a
deeper understanding of the underlying mathematical theory; and
developing new, intuitive and well-performing classification approaches.
The authors bridge the gap between the theoretical proposals of the
mathematical model and important challenges in machine learning. The
intended readership of this book includes anyone interested in learning
more about fuzzy rough set theory and how to use it in practical machine
learning contexts. Although the core audience chiefly consists of
mathematicians, computer scientists and engineers, the content will also
be interesting and accessible to students and professionals from a range
of other fields.