| 000 -LEADER |
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| fixed length control field |
nam a22 7a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
190527b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783319980737 |
| Terms of availability |
(hbk) |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
FER |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Fernandez, Alberto |
| 245 ## - TITLE STATEMENT |
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| Title |
Learning from imbalanced data sets |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Switzerland : |
| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2018 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xviii, 377 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm. |
| 365 ## - TRADE PRICE |
|---|
| Price type code |
EUR |
| Price amount |
119.99 |
| Unit of pricing |
00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Machine learning |
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| Topical term or geographic name as entry element |
Artificial intelligence |
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| Topical term or geographic name as entry element |
Data processing |
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| Topical term or geographic name as entry element |
Big data |
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| Topical term or geographic name as entry element |
Network hardware |
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| Topical term or geographic name as entry element |
Information systems |
|
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| Topical term or geographic name as entry element |
Computer science |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Garcia, Salvador |
| Relator term |
aut |
|
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| Personal name |
Galar, Mikel |
| Relator term |
aut |
|
|---|
| Personal name |
Prati, Ronaldo C. |
| Relator term |
aut |
|
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| Personal name |
Krawczyk, Bartosz |
| Relator term |
aut |
|
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| Personal name |
Herrera, Francisco |
| Relator term |
aut |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Item type |
Books |