Titlebook: Learning from Imbalanced Data Sets; Alberto Fernández,Salvador García,Francisco Herrer Book 2018 Springer Nature Switzerland AG 2018 Machi

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Alberto Fernández,Salvador García,Francisco HerrerOffers a comprehensive review of imbalanced learning widely used worldwide in many real applications, such as fraud detection, disease diagnosis, etc.Provides the user with the required background and

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Book 2018s 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 p

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osis, etc.Provides the user with the required background and.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

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Performance Measures,llows: First, Sect. 3.1 sets the background on the evaluation procedure. Then, Sect. 3.2 presents performance measures for crisp, nominal predictions. Section 3.3 discuss evaluation methods for scoring classifiers. Finally, Sect. 3.4 discuss probabilistic evaluation, and Sect. 3.5 concludes the chapter.

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Foundations on Imbalanced Classification,veral test beds where algorithms designed to address imbalanced classification problems can be compared. Some of these case studies will be considered in the remaining of this Book in order to analyze the behavior of the different methods discussed.

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Non-classical Imbalanced Classification Problems, 12.4 the problem of class imbalance when labels are associated to bags of instances, rather than individually (Multi-instance Learning), is analyzed. Next, Sect. 12.5 refers to the problem of class imbalance when there exists an ordinal relation among classes (Ordinal Classification). Finally, in Sect. 12.6 some concluding remarks are presented.

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Introduction to KDD and Data Science,sing the development of multiple software solutions for the treatment of data and integrating lots of Data Science algorithms. In order to better understand the nature of Data Science, this chapter is organized as follows. Sections 1.2 and 1.3 defines the Data Science terms and its workflow. Then, i