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Titlebook: Big Data Preprocessing; Enabling Smart Data Julián Luengo,Diego García-Gil,Francisco Herrera Book 2020 Springer Nature Switzerland AG 2020

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期刊全称Big Data Preprocessing
期刊简称Enabling Smart Data
影响因子2023Julián Luengo,Diego García-Gil,Francisco Herrera
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发行地址One of the first books on preprocessing in Big Data that covers a large amount of significant issues, namely the enumeration and description of some of the most recent solutions to address imbalanced
图书封面Titlebook: Big Data Preprocessing; Enabling Smart Data Julián Luengo,Diego García-Gil,Francisco Herrera Book 2020 Springer Nature Switzerland AG 2020
影响因子This book offers a comprehensible overview of  Big Data Preprocessing, which includes a formal description of each problem.  It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. .This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud..Finally, this book provides some novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing
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https://doi.org/10.1007/978-1-4614-5987-3and science. However, because of the myriad of existing tools, it is often difficult for practitioners and experts to analyze and select the correct tool for their problems. In this chapter we present an introductory summary to the wide environment of Big Data with the aim of providing necessary kno
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Comparative Analysis of Political Value,arises as a possible solution to enable large-scale learning with millions of dimensions. Nevertheless, as any other family of algorithms, reduction methods require an upgrade in its design so that they can work with such magnitudes. Particularly, they must be prepared to tackle the explosive combin
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Comparative Analysis of Political Cognition,pace and better define the decision boundaries between classes. Theoretically, reduction techniques should enable the application of learning algorithms on large-scale problems. Nevertheless, standard algorithms suffer from the increment on size and complexity of today’s problems. The objective of t
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