Mikel Galar

Francisco Herrera

Salvador García

Ronaldo C. Prati

Alberto Fernández

Bartosz Krawczyk

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.

Learning from Imbalanced Data Sets

Mauro Benente

Federico G. Thea

Un libro que promueve la necesidad de una reforma judicial profunda. Del lawfare a la figura del arrepentido, de la asociación ilícita al memorándum de entendimiento con Irán, La Justicia acusada da cuenta de la dimensión de la crisis de nuestra Justicia y ofrece alternativas de salida. Sin una Justicia independiente del poder político, no hay democracia ni República. Solo existe una corporación de jueces atentos a satisfacer el deseo del poderoso y a castigar sin razón a quienes lo enfrenten. En los últimos años, hemos visto el deterioro judicial, persecuciones indebidas, detenciones arbitrarias inducidas por quienes gobernaban y silenciadas por cierta complacencia mediática. Por eso, este libro viene a manifestar un contundente Nunca más. Nunca más a una Justicia contaminada por servicios de inteligencia, por "operadores judiciales", por procedimientos oscuros y linchamientos mediáticos. Nunca más a una Justicia que decide y persigue según los vientos políticos del poder del momento. Nunca más a una Justicia que es utilizada para saldar discusiones políticas o que judicializa los disensos para eliminar al adversario de turno. Lo digo con la firmeza de una decisión profunda: Nunca más es nunca más. Porque una Justicia demorada y manipulada significa una democracia acosada y denegada. Alberto Fernández

La Justicia acusada

João Gama

Pedro Henriques Abreu

Pedro Pereira Rodrigues

This book constitutes the proceedings of the 19th International Symposium on Intelligent Data Analysis, IDA 2021, which was planned to take place in Porto, Portugal. Due to the COVID-19 pandemic the conference was held online during April 26-28, 2021. The 35 papers included in this book were carefully reviewed and selected from 113 submissions. The papers were organized in topical sections named: modeling with neural networks; modeling with statistical learning; modeling language and graphs; and modeling special data formats.

19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26–28, 2021, Proceedings

Advances in Intelligent Data Analysis XIX

Alberto Fernández

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