nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Multiple Classifier Fusion Based on Testing Sample Pairs

verfasst von : Gaochao Feng, Deqiang Han, Yi Yang, Jiankun Ding

Erschienen in: Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

A new multiple classifier fusion approach is proposed, which use the classification based on Testing Sample Pairs (CTSP) as member classifiers. To make use of the potential information provided by the classifier, in this paper, CTSP classifier’s output is modeled with the fuzzy membership function. Then, in multiple classifier fusion, the fuzzy-cautious ordered weighted averaging approach with evidential reasoning (FCOWA-ER) is used to combine the membership functions generated by different member classifiers. Experimental results show that the proposed multiple classifier fusion approach can effectively improve the classification accuracy.

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 Applying Knowledge-Based Reasoning for Information Fusion in Intelligence, Surveillance, and Reconnaissance

Nächstes Kapitel Bayesian Estimator Based Target Localization in Ship Monitoring System Using Multiple Compact High Frequency Surface Wave Radars

Bi, Y., Guan, J., Bell, D.: The combination of multiple classifiers using an evidential reasoning approach. Artif. Intell. 172(15), 1731–1751 (2008)CrossRef

Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)CrossRef

Ding, J., Han, D., Yang, Y.: Design of multiple classifier systems based on shortest feature line segment. Hsi-An Chiao Tung Ta Hsueh/J. Xi’an Jiaotong Univ. 49(9), 77–83 (2015)

Radtke, P., Granger, E., Sabourin, R., Gorodnichy, D.: Adaptive ensemble selection for face re-identification under class imbalance. In: International Workshop on Multiple Classifier Systems, pp. 95–108. Springer, Heidelberg (2013)CrossRef

Suen, C.Y.: Recognition of handwritten numerals based on the concept of multiple experts. In: 1st International Workshop on Frontiers in Handwriting Recognition on Proceedings, pp. 131–144 (1999)

Pizzi, N.: Classification of biomedical spectra using stochastic feature selection. Neural Netw. World 15, 257–268 (2005)

Zhang, X.F., Wang, P.H., Feng, B., Du, L., Liu, H.W.: A new method to improve radar HRRP recognition and outlier rejection performances based on classifier combination. Zidonghua Xuebao/Acta Automatica Sinica 40(2), 348–356 (2014)

Kuncheva, L.I.: Switching between selection and fusion in combining classifiers: an experiment. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 32(2), 146–156 (2002)CrossRef

Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)MathSciNetMATH

10.

Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Machine Learning Proceedings, pp. 249–256 (1992)CrossRef

11.

Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 53(1–2), 23–69 (2003)CrossRef

12.

Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418–435 (1992)CrossRef

13.

Franke, J., Mandler, E.: A comparison of two approaches for combining the votes of cooperating classifiers. In: 11th IAPR International Conference on IEEE, Pattern Recognition, vol. II. Conference B: Pattern Recognition Methodology and Systems, Proceedings, pp. 611–614 (1992)

14.

Saerens, M., Fouss, F.: Yet another method for combining classifiers outputs: a maximum entropy approach. In: International Workshop on Multiple Classifier Systems, pp. 82–91. Springer, Heidelberg (2004)CrossRef

15.

Shafer, G.: A Mathematical Theory of Evidence, vol. 42. Princeton University Press, Princeton (1976)MATH

16.

Jaromczyk, J.W., Toussaint, G.T.: Relative neighborhood graphs and their relatives. Proc. IEEE 80(9), 1502–1517 (1992)CrossRef

17.

Chaudhuri, B.B.: A new definition of neighborhood of a point in multi-dimensional space. Pattern Recogn. Lett. 17(1), 11–17 (1996)CrossRef

18.

Li, S.Z., Lu, J.: Face recognition using the nearest feature line method. IEEE Trans. Neural Netw. 10(2), 439–443 (1999)CrossRef

19.

Han, D.Q., Han, C.Z., Yang, Y.: A novel classifier based on shortest feature line segment. Pattern Recogn. Lett. 32(3), 485–493 (2011)CrossRef

20.

Han, D., Han, C., Yang, Y.: A novel classification approach based on testing sample pairs. Inf. Int. Interdisc. J. 14(3), 693–698 (2011)MathSciNetMATH

21.

Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. In: Readings in Fuzzy Sets for Intelligent Systems, pp. 80–87 (1993)CrossRef

22.

Han, D., Dezert, J., Tacnet, J. M., Han, C.: A fuzzy-cautious OWA approach with evidential reasoning. In: 15th International Conference IEEE on Information Fusion (FUSION), pp. 278–285 (2012)

23.

Pedrycz, W.: Fuzzy sets in pattern recognition: methodology and methods. Pattern Recogn. 23(1–2), 121–146 (1990)CrossRef

24.

Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach. Learn. 40(2), 139–157 (2000)CrossRef

Titel: Multiple Classifier Fusion Based on Testing Sample Pairs
verfasst von: Gaochao Feng
Deqiang Han
Yi Yang
Jiankun Ding
Verlag: Springer International Publishing
Buch: Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System
Print ISBN: 978-3-319-90508-2

Electronic ISBN: 978-3-319-90509-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-90509-9_8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"