Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2016 | OriginalPaper | Chapter

1. Introduction

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

Published in: Multilabel Classification

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This book is focused on multilabel classification and related topics. Multilabel classification is one specific type of classification, classification being one of the usual tasks in the data mining field. Data mining itself can be seen as a step into a broad process, the discovery of new knowledge from databases. The goal of this first chapter is to introduce all these concepts, aiming to set the working context for the topics covered in the following ones. A global outline to this respect is given in Sect. 1.1. Section 1.2 provides an overview of the whole Knowledge Discovery in Databases process. Section 1.3 introduces the essential preprocessing tasks. Then, the different learning styles in use nowadays are explained in Sect. 1.4, and lastly multilabel classification is introduced in comparison with other traditional types of classification in Sect. 1.5.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
next chapter Multilabel Classification
Footnotes
1
The pictures used in this example are public domain images taken from the http://​www.​publicdomainpict​ures.​net website.
 
Literature
1.
Aggarwal, C.C. (ed.): Data Classification: Algorithms and Applications. CRC Press (2014)
2.
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB’94, pp. 487–499. Morgan Kaufmann (1994)
3.
Aha, D.W. (ed.): Lazy Learning. Springer (1997)
4.
5.
Barber, D.: Bayesian Reasoning and Machine Learning. Cambridge University Press (2012)
6.
Bielza, C., Li, G., Larrañaga, P.: Multi-dimensional classification with Bayesian networks. Int. J. Approximate Reasoning 52(6), 705–727 (2011)MathSciNetCrossRefMATH
7.
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC press (1984)
8.
Celebi, M.E., Aydin, K. (eds.): Unsupervised Learning Algoritms. Springer (2016)
9.
Cherkassky, V., Mulier, F.: Learning from Data: Concepts, Theory and Methods. Wiley-IEEE Press (2007)
10.
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATH
11.
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefMATH
12.
De Gooijer, J.G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22(3), 443–473 (2006)CrossRef
13.
Dong, G., Pei, J.: Sequence Data Mining. Springer (2007)
14.
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37 (1996)
15.
Fürnkranz, J., Gamberger, D., Lavrač, N.: Foundations of Rule Learning. Springer (2012)
16.
Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recogn. 44(8), 1761–1776 (2011)CrossRef
17.
García, S., Luengo, J., Herrera, F.: Data Preprocessing in Data Mining. Springer (2015)
18.
Gibaja, E., Ventura, S.: A tutorial on multi-label learning. ACM Comput. Surv. 47(3) (2015)
19.
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Jaufmann (2011)
20.
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8(1), 53–87 (2004)MathSciNetCrossRef
21.
Hernández-González, J., Inza, I., Lozano, J.A.: Weak supervision and other non-standard classification problems: a taxonomy. Pattern Recogn. Lett. 69, 49–55 (2016)CrossRef
22.
Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and practice. OText books (2013)
23.
Koller, D., Friedman, N.: Probabilistic Graphical Models. Principles and Techniques. MIT Press (2009)
24.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef
25.
Novak, P.K., Lavrač, N., Webb, G.I.: Supervised descriptive rule discovery: a unifying survey of contrast set, emerging pattern and subgroup mining. J. Mach. Learn. Res. 10, 377–403 (2009)MATH
26.
Quinlan, J.R.: C4.5: Programs for Machine Learning (1993)
27.
Rojas, R.: Neural Networks. A Systematic Study. Springer (1996)
28.
Rokach, K., Maimon, O.: Data Mining with Decision Trees, 2nd edn. World Scientific (2015)
29.
Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer (2010)
30.
Zhang, C., Zhang, S.: Association Rule Mining. Springer (2002)
31.
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)CrossRef
32.
33.
Zhu, X., Goldberg, A.B.: Introduction to Semi-supervised Learning. Morgan & Claypool Publishers (2009)
Metadata
Title
Introduction
Authors
Francisco Herrera
Francisco Charte
Antonio J. Rivera
María J. del Jesus
Copyright Year
2016
Book
Multilabel Classification

Print ISBN: 978-3-319-41110-1

Electronic ISBN: 978-3-319-41111-8

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-41111-8_1

Premium Partner

    Image Credits