Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

7. Dimensionality Reduction

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

Erschienen in: Multilabel Classification

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

High dimensionality is a profoundly studied problem in machine learning. Usually, a high-dimensional input space defies most classification algorithms, tending to produce more complex and less effective models. Multilabel data are also affected by high dimensionality in the output space, since many datasets have hundreds or even thousands of labels. This chapter aims to explain how high dimensionality affects multilabel classification, as well as the methods proposed to deal with this obstacle. A general overview of the curse of dimensionality in the multilabel field is provided in Sect. 7.1. Section 7.2 introduces feature space reduction techniques, outlining several specific proposals and testing how applying feature selection impacts multilabel classifiers results. Then, a similar discussion but related to label space dimensionality is given in Sect. 7.3, also including some experimental results. Section 7.4 summarizes the chapter.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel Ensemble-Based Classifiers
Nächstes Kapitel Imbalance in Multilabel Datasets
Literatur
1.
Bellman, R.: Dynamic Programming. P (Rand Corporation). Princeton University Press (1957)
2.
3.
Charte, F., Rivera, A., del Jesus, M.J., Herrera, F.: LI-MLC: a label inference methodology for addressing high dimensionality in the label space for multilabel classification. IEEE Trans. Neural Networks Learn. Syst. 25(10), 1842–1854 (2014)CrossRef
4.
Das, S.: Filters, wrappers and a boosting-based hybrid for feature selection. In: Proceedings of 18th International Conference on Machine Learning, ICML’01, pp. 74–81. Morgan Kaufmann (2001)
5.
Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997)CrossRef
6.
Dembszynski, K., Waegeman, W., Cheng, W., Hüllermeier, E.: On label dependence in multilabel classification. In: ICML Workshop on Learning from Multi-label Data, pp. 5–12 (2010)
7.
Doquire, G., Verleysen, M.: Mutual information-based feature selection for multilabel classification. Neurocomputing 122, 148–155 (2013)CrossRefMATH
8.
Fisher, R.A.: The statistical utilization of multiple measurements. Ann. Eugenics 8(4), 376–386 (1938)CrossRefMATH
9.
Guyon, I., Bitter, H.M., Ahmed, Z., Brown, M., Heller, J.: Multivariate non-linear feature selection with kernel multiplicative updates and Gram-Schmidt relief. In: Proceedings of International Joint Workshop on Soft Computing for Internet and Bioinformatics, BISC Flint-CIBI’03, pp. 1–11 (2003)
10.
Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A. (eds.): Feature Extraction: Foundations and Applications. Springer (2008)
11.
Hotelling, H.: Relations between two sets of variates. In: Breakthroughs in Statistics, pp. 162–190. Springer (1992)
12.
Hsu, D., Kakade, S., Langford, J., Zhang, T.: Multi-label prediction via compressed sensing. In: Proceedings of 22th Annual Conference on Advances in Neural Information Processing Systems, NIPS’09, vol. 22, pp. 772–780 (2009)
13.
Jolliffe, I.: Principal Component Analysis. Springer Series in Statistics, vol. 1. Springer, Berlin (1986)
14.
Kira, K., Rendell, L.A.: The feature selection problem: traditional methods and a new algorithm. In: Proceedings of 10th National Conference on Artificial Intelligence, AAAI’92, pp. 129–134. AAAI Press (1992)
15.
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1), 273–324 (1997)CrossRefMATH
16.
Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Machine Learning: ECML-94, pp. 171–182 (1994)
17.
Lee, J.S., Kim, D.W.: Mutual information-based multi-label feature selection using interaction information. Expert Syst. Appl. 42, 2013–2025 (2015)CrossRef
18.
Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining, vol. 454. Springer Science & Business Media (2012)
19.
Read, J.: A pruned problem transformation method for multi-label classification. In: Proceedings of New Zealand Computer Science Research Student Conference, NZCSRS’08, pp. 143–150 (2008)
20.
Read, J., Pfahringer, B., Holmes, G.: Multi-label classification using ensembles of pruned sets. In: Proceedings of 8th IEEE International Conference on Data Mining, ICDM’08, pp. 995–1000. IEEE (2008)
21.
Spolaor, N., Cherman, E.A., Monard, M.C., Lee, H.D.: A comparison of multi-label feature selection methods using the problem transformation approach. Electron. Notes Theor. Comput. Sci. 292, 135–151 (2013)CrossRef
22.
Sun, L., Ji, S., Ye, J.: Hypergraph spectral learning for multi-label classification. In: Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 668–676. ACM (2008)
23.
Sun, L., Ji, S., Ye, J.: Multi-Label Dimensionality Reduction. CRC Press (2013)
24.
Tsoumakas, G., Katakis, I., Vlahavas, I.: Effective and efficient multilabel classification in domains with large number of labels. In: Proceedings of ECML/PKDD Workshop on Mining Multidimensional Data, MMD’08, pp. 30–44 (2008)
25.
Tsoumakas, G., Vlahavas, I.: Random k-Labelsets: an ensemble method for multilabel classification. In: Proceedings of 18th European Conference on Machine Learning, ECML’07, vol. 4701, pp. 406–417. Springer (2007)
26.
Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V.: Kernel dependency estimation. In: Proceedings of 16th Annual Conference on Advances in Neural Information Processing Systems, NIPS’02, vol. 15, pp. 873–880 (2002)
27.
Wyse, N., Dubes, R., Jain, A.K.: A critical evaluation of intrinsic dimensionality algorithms. Pattern Recogn. Pract. 415–425 (1980)
28.
Yu, K., Yu, S., Tresp, V.: Multi-label informed latent semantic indexing. In: Proceedings of 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 258–265. ACM (2005)
29.
Zhang, M., Zhou, Z.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)CrossRef
30.
Zhang, Y., Zhou, Z.H.: Multilabel dimensionality reduction via dependence maximization. ACM Trans. Knowl. Discovery Data (TKDD) 4(3), 14 (2010)
31.
Zhao, Z., Liu, H.: Semi-supervised feature selection via spectral analysis. In: Proceedings of 7th SIAM International Conference on Data Mining, SDM’07, pp. 641–646 (2007)
32.
Zhou, T., Tao, D., Wu, X.: Compressed labeling on distilled labelsets for multi-label learning. Mach. Learn. 88(1–2), 69–126 (2012)MathSciNetCrossRefMATH
Metadaten
Titel
Dimensionality Reduction
verfasst von
Francisco Herrera
Francisco Charte
Antonio J. Rivera
María J. del Jesus
Copyright-Jahr
2016
Buch
Multilabel Classification

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

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

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-41111-8_7

Premium Partner

    Bildnachweise