This book provides a general and comprehensible overview of imbalanced learning. It contains a
formal description of a problem and focuses on its main features and the most relevant
proposed solutions. Additionally it considers the different scenarios in Data Science for
which the imbalanced classification can create a real challenge. This book stresses the gap
with standard classification tasks by reviewing the case studies and ad-hoc performance metrics
that are applied in this area. It also covers the different approaches that have been
traditionally applied to address the binary skewed class distribution. Specifically it reviews
cost-sensitive learning data-level preprocessing methods and algorithm-level solutions taking
also into account those ensemble-learning solutions that embed any of the former alternatives.
Furthermore it focuses on the extension of the problem for multi-class problems where the
former classical methods are no longer to be applied in a straightforward way.This book also
focuses on the data intrinsic characteristics that are the main causes which added to the
uneven class distribution truly hinders the performance of classification algorithms in this
scenario. Then some notes on data reduction are provided in order to understand the advantages
related to the use of this type of approaches.Finally this book introduces some novel areas of
study that are gathering a deeper attention on the imbalanced data issue. Specifically it
considers the classification of data streams non-classical classification problems and the
scalability related to Big Data. Examples of software libraries and modules to address
imbalanced classification are provided.This book is highly suitable for technical professionals
senior undergraduate and graduate students in the areas of data science computer science and
engineering. It will also be useful for scientists and researchers to gain insight on
thecurrent developments in this area of study as well as future research directions.
