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
Progress in Artificial Intelligence
Erschienen in: Progress in Artificial Intelligence 4/2016

06.08.2016 | Regular Paper

An ensemble-based approach for multi-view multi-label classification

verfasst von: Eva L. Gibaja, Jose M. Moyano, Sebastián Ventura

Erschienen in: Progress in Artificial Intelligence | Ausgabe 4/2016

Einloggen

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

search-config
loading …

Abstract

Multi-label classification with multiple data views is a recent research field not much explored. This more flexible learning approach allows each pattern to be represented by several sets of attributes and each pattern can have simultaneously associated several labels. In this work, an ensemble-based approach, which enables the fusion of views at decision level by majority voting, is proposed. The study carried out on four data sets considering 27 multi-label evaluation metrics shows that our proposal overcomes and improves the results obtained by the individual views as well as the execution time and the performance of the classic approach which concatenates all the views in a single set of features.
Vorheriger Artikel WSCISOM: wireless sensor data cluster identification through a hybrid SOM/MLP/RBF architecture
Nächster Artikel Fuzzy sets across the natural language generation pipeline

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

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

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
Literatur
1.
Charte, F., Rivera, A.J., 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 Netw. Learn. Syst. 25(10), 1842–1854 (2014)CrossRef
2.
Chen, Q., Sun, S.: Hierarchical multi-view fisher discriminant analysis. In: Leung, C.-S. Lee, M. Chan, J.H. (eds.) Neural Information Processing, pp. 289–298. Springer, Berlin (2009)
3.
Cheng, W., Hüllermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Mach. Learn. 76(2–3), 211–225 (2009)CrossRef
4.
Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 42–53. PKDD ’01, Springer-Verlag, London, UK (2001)
5.
Elisseeff, A., Weston, J.: Kernel methods for multi-labelled classification and categorical regression problems. Adv. Neural Inform. Process. Syst. 14, 681–687 (2001)
6.
Farquhar, J., Hardoon, D., Meng, H., Shawe-taylor, J.S., Szedmak, S.: Two view learning: SVM-2K, theory and practice. In: Weiss, Y., Sch ölkopf B., Platt J.C. (eds.) Advances in Neural Information Processing Systems, 18, pp. 355–362. MIT Press (2005)
7.
Fürnkranz, J., Hüllermeier, E., Mencía, E.L., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008)CrossRef
8.
Gibaja, E., Ventura, S.: Multi-label learning: a review of the state of the art and ongoing research. Wiley Interdiscip. Rev. Data Mining Knowl. Discov. 4(6), 411–444 (2014)CrossRef
9.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)CrossRef
10.
He, J., Lawrence, R.: A graph-based framework for multi-task multi-view learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 25–32 (2011)
11.
Jin, X., Zhuang, F., Wang, S., He, Q., Shi, Z.: Shared structure learning for multiple tasks with multiple views. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, vol. 8189, pp. 353–368. Springer, Berlin (2013)
12.
Luo, Y., Tao, D., Xu, C., Xu, C., Liu, H., Wen, Y.: Multiview vector-valued manifold regularization for multilabel image classification. IEEE Trans. Neural Netw. Learn. Syst. 24(5), 709–722 (2013)CrossRef
13.
Madjarov, G., Kocev, D., Gjorgjevikj, D., Džeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recognit. 45(9), 3084–3104 (2012)CrossRef
14.
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)CrossRefMATH
15.
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 1–27 (2011)MathSciNetCrossRef
16.
Read, J.: A pruned problem transformation method for multi-label classification. In: Proceedings of the NZ Computer Science Research Student Conference, pp. 143–150 (2008)
17.
Sechidis, K., Tsoumakas, G., Vlahavas, I.: On the stratification of multi-label data. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) Machine Learning and Knowledge Discovery in Databases, pp. 145–158. Springer, Berlin (2011)
18.
Snoek, C.G., Worring, M., Smeulders, A.W.: Early versus late fusion in semantic video analysis. In: Proceedings of the 13th annual ACM international conference on Multimedia, pp. 399–402. ACM (2005)
19.
Sun, S.: A survey of multi-view machine learning. Neural Comput. Appl. 23(7–8), 2031–2038 (2013)CrossRef
20.
Sun, S., Zhang, Q.: Multiple-view multiple-learner semi-supervised learning. Neural Process. Lett. 34(3), 229–240 (2011)CrossRef
21.
Szedmak, S., Shawe-Taylor, J.: Synthesis of maximum margin and multiview learning using unlabeled data. Neurocomputing 70(7), 1254–1264 (2007)CrossRef
22.
Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multi-label classification of music into emotions. EURASIP J. Audio Speech Music Process. 2011(1), 4 (2011)CrossRef
23.
Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multi-label classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079–1089 (2010)CrossRef
24.
Tsoumakas, G., Dimou, A., Spyromitros, E., Mezaris, V., Kompatsiaris, I., Vlahavas, I.: Correlation-based pruning of stacked binary relevance models for multi-label learning. In: Proceedings of the 1st International Workshop on Learning from Multi-Label Data, pp. 101–116 (2009)
25.
Tsoumakas, G., Spyromitros-Xioufis, E., Vilcek, J., Vlahavas, I.: Mulan: a java library for multi-label learning. J. Mach. Learn. Res. 12, 2411–2414 (2011)MathSciNetMATH
26.
27.
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. ArXiv.org (Cornell University) (2013)
28.
Xu, J.: Fast multi-label core vector machine. Pattern Recognit. 46(3), 885–898 (2013)CrossRefMATH
29.
30.
Xu, Z., Sun, S.: An algorithm on multi-view adaboost. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) Neural Information Processing. Theory and Algorithms, pp. 355–362. Springer, Berlin (2010)
31.
Zhang, J., Huan, J.: Inductive multi-task learning with multiple view data. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 543–551. ACM (2012)
32.
Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338–1351 (2006)CrossRef
33.
Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit. 40(7), 2038–2048 (2007)CrossRefMATH
34.
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)CrossRef
35.
Zhang, Q., Sun, S.: Multiple-view multiple-learner active learning. Pattern Recognit. 43(9), 3113–3119 (2010)CrossRefMATH
36.
Zhou, T., Tao, D., Wu, X.: Compressed labeling on distilled labelsets for multi-label learning. Mach. Learn. 88(1–2), 69–126 (2012)MathSciNetCrossRefMATH
37.
Zou, F., Liu, Y., Wang, H., Song, J., Shao, J., Zhou, K., Zheng, S.: Multi-view multi-label learning for image annotation. Multimed. Tools Appl. 1–18 (2015). doi:10.​1007/​s11042-014-2423-2
Metadaten
Titel
An ensemble-based approach for multi-view multi-label classification
verfasst von
Eva L. Gibaja
Jose M. Moyano
Sebastián Ventura
Publikationsdatum
06.08.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Progress in Artificial Intelligence / Ausgabe 4/2016
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-016-0098-9

Weitere Artikel der Ausgabe 4/2016

Progress in Artificial Intelligence 4/2016 Zur Ausgabe

Regular Paper

Vine copula classifiers for the mind reading problem

Regular Paper

A comparative analysis of multi-criteria decision-making methods

Regular Paper

Applying multi-label and multi-class classification to enhance K-anonymity in sequential releases

Regular Paper

Fuzzy sets across the natural language generation pipeline

Regular Paper

WSCISOM: wireless sensor data cluster identification through a hybrid SOM/MLP/RBF architecture

Regular Paper

Data mining for software engineering and humans in the loop