000			nam a22 7a 4500
999			_c29547 _d29547
008			190527b xxu||||| |||| 00| 0 eng d
020			_a9783319980737 _c(hbk)
082			_a006.31 _bFER
100			_aFernandez, Alberto
245			_aLearning from imbalanced data sets
260			_aSwitzerland : _bSpringer, _c2018
300			_axviii, 377 p. : _bill. ; _c24 cm.
365			_aEUR _b119.99 _d00
504			_aIncludes bibliographical references.
520			_aThis 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.
650			_aMachine learning
650			_aArtificial intelligence
650			_aData processing
650			_aBig data
650			_aNetwork hardware
650			_aInformation systems
650			_aComputer science
700			_aGarcia, Salvador _eaut
700			_aGalar, Mikel _eaut
700			_aPrati, Ronaldo C. _eaut
700			_aKrawczyk, Bartosz _eaut
700			_aHerrera, Francisco _eaut
942			_2ddc _cBK