Imbalanced Learning: Foundations, Algorithms, and Applications
By: and
Sign Up Now!
Already a Member? Log In
You must be logged into Bookshare to access this title.
Learn about membership options,
or view our freely available titles.
- Synopsis
- The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications.
- Copyright:
- 2013
Book Details
- Book Quality:
- Publisher Quality
- Book Size:
- 216 Pages
- ISBN-13:
- 9781118646335
- Related ISBNs:
- 9781118074626
- Publisher:
- Wiley
- Date of Addition:
- 03/21/18
- Copyrighted By:
- The Institute of Electrical and Electronics Engineers, Inc.
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Technology
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.