Overview
- Covers key concepts of multilabel data characterization, treatment, and evaluation
- Equips the reader with all the software tools needed to handle multilabel data, including step-by-step instructions for use
- Provides the perfect guide for beginners and practitioners with interest in the topic, as well as experts seeking a comprehensive overview
- Includes supplementary material: sn.pub/extras
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About this book
• The special characteristics of multi-labeled data and the metrics available to measure them.
• The importance of taking advantage of label correlations to improve the results.
• The different approaches followed to face multi-label classification.
• The preprocessing techniques applicable to multi-label datasets.
• The available software tools to work with multi-label data.
This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.
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Table of contents (9 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Multilabel Classification
Book Subtitle: Problem Analysis, Metrics and Techniques
Authors: Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
DOI: https://doi.org/10.1007/978-3-319-41111-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-41110-1Published: 22 August 2016
Softcover ISBN: 978-3-319-82269-3Published: 22 April 2018
eBook ISBN: 978-3-319-41111-8Published: 09 August 2016
Edition Number: 1
Number of Pages: XVI, 194
Number of Illustrations: 72 b/w illustrations
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence