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2015 | Vol. 24 | 107--112
Tytuł artykułu

Dynamical ensemble selection - experimental analysis on homogenous pool of classifiers

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the dynamic ensemble selection based on the analysis of the decision profiles. These profiles are obtained from a posteriori probability functions returned from the base classifiers during the training process. Presented in the paper dynamic ensemble selection algorithms are dedicated to the binary classification task. In order to verify these algorithms, a number of experiments have been carried out on several medical data sets. The proposed dynamic ensemble selection is experimentally compared against the ensemble with the sum fusion method. As base classifiers we used the pool of homogeneous classifiers. The obtained results are promising because we could improve the classification accuracy of the ensemble classifier.
Wydawca

Rocznik
Tom
Strony
107--112
Opis fizyczny
Bibliogr. 20 poz., tab.
Twórcy
  • Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
autor
  • Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland , robert.burduk@pwr.edu.pl
Bibliografia
  • [1] BISHOP C. Bishop pattern recognition and machine learning. 2006. Springer, New York.
  • [2] BRITTO A. S., SABOURIN R., OLIVEIRA L. E. Dynamic selection of classifiersa comprehensive review. Pattern Recognition, 2014, Vol. 47. Elsevier, pp. 3665–3680.
  • [3] CAVALIN P. R., SABOURIN R., SUEN C. Y. Dynamic selection approaches for multiple classifier systems. Neural Computing and Applications, 2013, Vol. 22. Springer, pp. 673–688.
  • [4] CYGANEK B. One-class support vector ensembles for image segmentation and classfication. Journal of Mathematical Imaging and Vision, 2012, Vol. 42. Springer, pp. 103–117.
  • [5] DEMŠAR J. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 2006, Vol. 7. JMLR. org, pp. 1–30.
  • [6] DIDACI L., GIACINTO G., ROLI F., MARCIALIS G. L. A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognition, 2005, Vol. 38. Elsevier, pp. 2188–2191.
  • [7] FRANK A., ASUNCION A., ET AL. Uci machine learning repository. 2010.
  • [8] GIACINTO G., ROLI F. An approach to the automatic design of multiple classifier systems. Pattern recognition letters, 2001, Vol. 22. Elsevier, pp. 25–33.
  • [9] HO T. K., HULL J. J., SRIHARI S. N. Decision combination in multiple classfiier systems. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1994, Vol. 16. IEEE, pp. 66–75.
  • [10] JACKOWSKI K., KRAWCZYK B., WO´ZNIAK M. Improved adaptive splitting and selection: The hybrid training method of a classifier based on a feature space partitioning. International journal of neural systems, 2014, Vol. 24. World Scientific, p. 1430007.
  • [11] JACKOWSKI K., WOZNIAK M. Method of classifier selection using the genetic approach. Expert Systems, 2010, Vol. 27. Wiley Online Library, pp. 114–128.
  • [12] KITTLER J., ALKOOT F. M. Sum versus vote fusion in multiple classifier systems. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2003, Vol. 25. IEEE, pp. 110–115.
  • [13] KUNCHEVA L. I. A theoretical study on six classifier fusion strategies. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, no. 2. IEEE, pp. 281–286.
  • [14] KUNCHEVA L. I. Combining pattern classifiers: methods and algorithms. 2004. John Wiley & Sons.
  • [15] LAM L., SUEN C. Y. Application of majority voting to pattern recognition: an analysis of its behavior and performance. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 1997, Vol. 27. IEEE, pp. 553–568.
  • [16] RANAWANA R., PALADE V. Multi-classifier systems: Review and a roadmap for developers. Int. J. Hybrid Intell. Syst., 2006, Vol. 3. pp. 35–61.
  • [17] RUTA D., GABRYS B. Classifier selection for majority voting. Information fusion, 2005, Vol. 6. Elsevier, pp. 63–81.
  • [18] SMETEK M., TRAWI´NSKI B. Selection of heterogeneous fuzzy model ensembles using self-adaptive genetic algorithms. New Generation Computing, 2011, Vol. 29. Springer, pp. 309–327.
  • [19] SUEN C. Y., LEGAULT R., NADAL C., CHERIET M., LAM L. Building a new generation of handwriting recognition systems. Pattern Recognition Letters, 1993, Vol. 14. Elsevier, pp. 303–315.
  • [20] WOLOSZYNSKI T., KURZYNSKI M. A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recognition, 2011, Vol. 44. Elsevier, pp. 2656–2668.
Typ dokumentu
Bibliografia
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Identyfikator YADDA
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