Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus

Multilabel Classification (eBook, PDF)

Problem Analysis, Metrics and Techniques

• Format: PDF

Produktbeschreibung

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:

.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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 194
• Erscheinungstermin: 9. August 2016
• Englisch
• ISBN-13: 9783319411118
• Artikelnr.: 46922866

Inhaltsangabe

Introduction.- Multilabel Classification.- Case Studies and Metrics.- Transformation based Classifiers.- Adaptation based Classifiers.- Ensemble based Classifiers.- Dimensionality Reduction.- Imbalance in Multilabel Datasets.- Multilabel Software.