Learning from Imbalanced Data Sets

By Fanghong Guo, Yong-Duan Song, Changyun Wen

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.

• Format: Paperback
• Language: English
• ISBN: 3030074463
• ISBN13: 9783030074463
• Release Date: January 2019
• Publisher: Springer
• Length: 377 Pages
• Weight: 1.22 lbs.
• Dimensions: 0.8" x 6.1" x 9.2"

Customer Reviews

Computer Reference Books

I received Excel X for Mac OS X in excellent condition. Love it. Thank you.

Great for beginners

This is the second book I've purchased written by Maria Langer. The first was Mac OS X Panther (Visual QuickStart Guide). Like the book for panther. Excel X is great for those new to the program, or for those who have little experience in formating the spreadsheets, conjuring up formulas, and establish and managing lists. For more experienced/advanced users, you'll appreciate the book as quick reference and the nice tips...

Excellent way to quickly transfer your Excel skills from Wintel to Mac.

This book probably works just as well for an introduction to Excel for the ones who have no experience with this program. Given that I have extensive experience with Excel, my review focuses on this guide's ability to transfer your Excel skills from the Wintel to the Mac OS environment. This guide does a very good job of transferring the skills you have acquired in Excel/Wintel to Excel/Mac OS in the minimum of time. The...

Mac users do well to get Windows Excel books

I'm a long-time (since 1984) Mac user and devotee, so I'm not being heretical when I say that the best books about Excel are written for a Windows audience. The Mac versions of Excel are sufficiently compatible with the Windows versions that almost everything you learn from a Windows book will apply on the Mac. There are so many more Windows users (and especially power users) of Excel that many more books have been written...