This&nbsp; book provides a general and comprehensible&nbsp;overview of&nbsp;&nbsp; imbalanced learning.&nbsp; It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers&nbsp;the different scenarios in Data Science for which the imbalanced classification can&nbsp;create a real challenge.&nbsp; This book stresses the gap with standard classification tasks by reviewing the case&nbsp;studies and ad-hoc performance metrics that are applied in this area. It also covers the&nbsp;different approaches that have been traditionally applied to address the binary&nbsp;skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level&nbsp;preprocessing methods and algorithm-level solutions, taking also into account those&nbsp;ensemble-learning solutions that embed any of the former alternatives. Furthermore, it&nbsp;focuses on the extension of the problem for multi-class problems, where the former&nbsp;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&nbsp;which, added to the uneven class distribution, truly hinders the performance of&nbsp;classification algorithms in this scenario. Then, some notes on data reduction are&nbsp;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&nbsp;on the imbalanced data issue. Specifically, it considers the classification of data streams,&nbsp;non-classical classification problems, and the scalability related to Big Data. Examples&nbsp;of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior&nbsp;undergraduate and graduate&nbsp;students in the areas of data science,&nbsp;computer science and engineering.&nbsp;&nbsp;It will also be useful for scientists and researchers to gain insight on the current&nbsp;developments in this area of study, as well as future research directions.&nbsp;

Learning from Imbalanced Data Sets

Computers & Technology

García, Salvador

Herrera, Francisco

Fernández, Alberto

Galar, Mikel

Prati, Ronaldo C.

Krawczyk, Bartosz

Computer Science

Springer

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.
This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Data Preprocessing in Data Mining (Intelligent Systems Reference Library, 72)

Luengo, Julián

Los Amantes de Teruel es obra romantica en el fondo y en la forma. Recoge una tradicion nacional llevada ya a la escena por Rey de Artieda, Tirso y Perez de Montalban. La fuerza del amor mueve a los personajes y ocasiona su desdicha; por amor mueren Isabel y Marsilla, se venga Zulima, amenaza Azagra y peca dona Margarita. Problema de honor es el proyectado lance entre don Martin y don Pedro; el de dona Margarita y la deshonra familiar; el del Sultan quien liberta a Marsilla y da muerte a la esposa infiel. Finalmente, honor, virtud y sentido del deber dictan a Isabel a rechazar a Marsilla. La ficcion se entrelaza con el fondo historico de la Espana medieval.

Los amantes de Teruel (Spanish Edition)

Hartzenbusch, Juan Eugenio

Humor & Entertainment

Castalia Ediciones

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