Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontec
Multilabel Classification : Problem Analysis, Metrics and Techniques
β Scribed by Francisco Herrera, Francisco Charte, Antonio J. Rivera, MarΓa J. del Jesus (auth.)
- Publisher
- Springer International Publishing
- Year
- 2016
- Tongue
- English
- Leaves
- 200
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are:
β’ The special characteristics of multi-labeled data and the metrics available to measure them.β’ The importance of taking advantage of label correlations to improve the results.β’ The different approaches followed to face multi-label classification.β’ The preprocessing techniques applicable to multi-label datasets.β’ 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.
β¦ Table of Contents
Front Matter....Pages i-xvi
Introduction....Pages 1-16
Multilabel Classification....Pages 17-31
Case Studies and Metrics....Pages 33-63
Transformation-Based Classifiers....Pages 65-79
Adaptation-Based Classifiers....Pages 81-99
Ensemble-Based Classifiers....Pages 101-113
Dimensionality Reduction....Pages 115-131
Imbalance in Multilabel Datasets....Pages 133-151
Multilabel Software....Pages 153-191
Back Matter....Pages 193-194
β¦ Subjects
Data Mining and Knowledge Discovery;Artificial Intelligence (incl. Robotics)
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