Multilabel Classification : Problem Analysis, Metrics and Techniques / by Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus.

Format:

Book

Author/Creator:

Herrera, Francisco (Computer scientist), author.

Charte Ojeda, Francisco, author.

Rivera, Antonio J., author.

del Jesus, María J., author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Data mining.

Artificial intelligence.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Local Subjects:

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Physical Description:

1 online resource (XVI, 194 pages) : 72 illustrations

Edition:

First edition 2016.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2016.

System Details:

text file PDF

Summary:

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.

Contents:

Introduction

Multilabel Classification

Case Studies and Metrics

Transformation based Classifiers

Adaptation based Classifiers

Ensemble based Classifiers

Dimensionality Reduction

Imbalance in Multilabel Datasets

Multilabel Software.

Other Format:

Printed edition:

ISBN:

978-3-319-41111-8

9783319411118

Access Restriction:

Restricted for use by site license.