| Notas: | "After a gentle introduction to the KDD process and current state of Data Science in the first chapter, the book then stresses the gap with standard classification tasks by establishing the foundations and reviewing the case studies with a direct application in this area in Chap. 2. Then, Chap. 3, introduces the main ad hoc evaluation metrics to be considered in this area of study. The book also covers skewed class distribution. Specifically, it reviews cost-sensitive learning (Chap. 4), data-level preprocessing methods (Chap. 5), and algorithm-level solutions (Chap. 6), taking also into account those ensemble-learning solutions that embed any of the former alternatives (Chap. 7). Furthermore, it focuses in Chap. 8 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. The book includes in Chap. 9 some notes on data reduction, being provided in order to understand the advantages related to the use of this type of approaches. Then, Chap. 10 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. Finally, this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, Chap. 11 considers the classification of data streams, Chap 12 the non-classical classification problems, and finally Chap. 13 discusses the scalability related to Big Data. To sum up, some examples of software libraries and modules to address imbalanced classification are given in Chap. 14"--Prefacio (páginas VII-VIII). |
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