Multilabel Classification de Charte, Francisco Format Relié

Multilabel Classification - Charte, Francisco

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Brand new, In English, Fast shipping from London, UK; Tout neuf, en anglais, expédition rapide depuis Londres, Royaume-Uni;ria9783319411101_dbm

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Présentation Multilabel Classification de Charte, Francisco Format Relié
- Livre Informatique

Livre Informatique - Charte, Francisco - 01/08/2016 - Relié - Langue : Anglais

Auteur(s) : Charte, Francisco - Herrera, Francisco - Rivera, Antonio J. - del Jesus, María J.

Editeur : Springer International Publishing Ag

Langue : Anglais

Parution : 01/08/2016

Format : Moyen, de 350g à 1kg

Nombre de pages : 212

Expédition : 489

Dimensions : 24.1 x 16.0 x 1.8

ISBN : 9783319411101

Résumé :

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.

...

Biographie:
Juli?n Luengo received the M.S. degree in computer science and the Ph.D. from the University of Granada, Granada, Spain, in 2006 and 2011 respectively. He currently acts as an Assistant Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada, Spain. His research interests include machine learning and data mining, data preparation in knowledge discovery and data mining, missing values, noisy data, data complexity and fuzzy systems. Dr. Luengo has been given some awards and honors for his personal work or for his publications in and conferences, such as IFSA-EUSFLAT 2009 Best Student Paper Award. He belongs to the list of the Highly Cited Researchers in the area of Computer Sciences (2015- 2018) (Clarivate Analytics).

Diego Garc??a-Gil received the M.Sc. degree in computer science from the University of Granada, Granada, Spain, in 2015. He is currently pursuing the Ph.D. degree with the Department ofComputer Science and Artificial Intelligence, University of Granada, Granada, Spain. His current research interests include machine learning, data mining, data preprocessing and Big Data.

Sergio Ram?rez-Gallego received the M.Sc. degree in computer science from the University of Ja?n, Ja?n, Spain, in 2012. He obtained the Ph.D. degree with the Department of Computer Science and Artificial Intelligence, University of Granada, Spain in 2018. His current research interests include data mining, data preprocessing, big data, and cloud computing.

Salvador Garc?a received the B.S. and Ph.D. degrees in Computer Science from the University of Granada, Granada, Spain, in 2004 and 2008, respectively. He is currently an Associate Professor in the Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain. Dr. Garc?a has published more than 80 papers in international journals (more than60 in Q1), h-index 43, over 60 papers in international conference proceedings (data from Web of Science). He has organized several special sessions and workshops related to data preprocessing and evolutionary learning in conferences such as Hybrid Intelligent Systems, Intelligent Systems Design and Applications and International Joint-Conference of Neural Networks. He has been associated with the international program committees and organizing committees of several regular international conferences including IEEE CEC, ICPR, ICDM, IJCAI, etc. As edited activities, he has co-edited two special issues in international journals and he is an associate editor of Information Fusion (Elsevier), Swarm and Evolutionary Computation (Elsevier) and AI Communications (IOS Press) journals, and he is co-Editor in Chief of the international journal Progress in Artificial Intelligence (Springer). He is a co-author of the books entitled Data Preprocessing in Data Mining and Learning fromImbalanced Data Sets published by Springer. His research interests include data science, data preprocessing, Big Data, evolutionary learning, Deep Learning, metaheuristics and biometrics.

Francisco Herrera (SM'15) received his M.Sc. in Mathematics in 1988 and Ph.D. in Mathematics in 1991, both from the University of Granada, Spain. He is currently a Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada and Director of DaSCI Institute (Andalusian Research Institute in Data Science and Computational Intelligence). He has been the supervisor of 44 Ph.D. students. He has published more than 400 journal papers, receiving more than 66000 citations (Scholar Google, H-index 132). He is co-author of the books Genetic Fuzzy Systems (World Scientific, 2001) and Data Preprocessing in...

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

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Multilabel Classification - Charte, Francisco

Avis sur Multilabel Classification de Charte, Francisco Format Relié - Livre Informatique

Présentation Multilabel Classification de Charte, Francisco Format Relié - Livre Informatique

Présentation Multilabel Classification de Charte, Francisco Format Relié
- Livre Informatique