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Books > Computing & IT > Computer communications & networking
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Learning from Imbalanced Data Sets (Paperback, Softcover reprint of the original 1st ed. 2018)
Loot Price: R4,258
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Learning from Imbalanced Data Sets (Paperback, Softcover reprint of the original 1st ed. 2018)
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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 the current developments in this
area of study, as well as future research directions.
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