國立虎尾科技大學 |

Multilabel Classification = Problem Analysis, Metrics and Techniques /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Multilabel Classification / by Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus.
其他題名:	Problem Analysis, Metrics and Techniques /
作者:	Herrera, Francisco.
其他作者:	Charte, Francisco.
面頁冊數:	XVI, 194 p. 72 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-3-319-41111-8
ISBN:	9783319411118

Multilabel Classification = Problem Analysis, Metrics and Techniques /
Herrera, Francisco.

Multilabel Classification Problem Analysis, Metrics and Techniques /[electronic resource] :by Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus. - 1st ed. 2016. - XVI, 194 p. 72 illus.online resource.

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

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.

ISBN: 9783319411118

Standard No.: 10.1007/978-3-319-41111-8doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Multilabel Classification = Problem Analysis, Metrics and Techniques /
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