The proposal of calculation classifier weights for an assembly of classifiers

• Autorzy: Burduk R.
• Języki publikacji: EN

Abstrakty

EN

The selection of classifiers is one of the important problems in the creation of ensemble of classifiers. The paper presents the static selection in which a new method of calculating the weights of individual classifiers is used. The obtained weights can be interpreted in the context of the interval logic. It means that the particular weights will not be provided precisely but their lower and upper values will be used. A number of experiments have been carried out on several medical data sets.

Słowa kluczowe

EN

classifier fusion; static classifiers selection; multiple classifier system

PL

fuzja informacji; fuzja klasyfikatorów; selekcja klasyfikatorów; system klasyfikujący wielokrotny

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2014
• Tom: Vol. 23
• Strony: 181--186
• Opis fizyczny: Bibliogr. 12 poz., tab.

Twórcy

autor

Burduk R.

• Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland

Bibliografia

• [1] BURDUK R., WOŹNIAK M., Different decision tree induction strategies for a medical decision problem. Central European Journal of Medicine, 2012, Vol. 7 (2), pp. 183–193.
• [2] CAVALIN P. R., SABOURIN R., SUEN. C. Y., Dynamic selection approaches for multiple classifier systems. Neural Computing and Applications, 2013, Vol. 22 (3-4), pp. 673–688.
• [3] 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, 11/2005, Vol. 38, pp. 2188–2191.
• [4] DOS SANTOS E. M., SABOURIN R., Classifier ensembles optimization guided by population oracle. In IEEE Congress on Evolutionary Computation, 2011, pp. 693–698.
• [5] FRANK A., ASUNCION A., UCI machine learning repository, 2010.
• [6] JACKOWSKI K., KRAWCZYK B., WOŹNIAK 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 (03).
• [7] JACKOWSKI K., WOŹNIAK M., Method of classifier selection using the genetic approach. Expert Systems, 2010, Vol. 27 (2), pp. 114–128.
• [8] KUNCHEVA L. I., Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc., 2004.
• [9] KURZYŃSKI M. W., Diagnosis of acute abdominal pain using a three-stage classifier. Computers in biology and medicine, 1987, Vol. 17 (1), pp. 19–27.
• [10] RUTA D., GABRYS B., Classifier selection for majority voting. Information Fusion, 2005, Vol. 6 (1), pp. 63–81.
• [11] SMETEK M., TRAWIŃSKI B., Selection of heterogeneous fuzzy model ensembles using self-adaptive genetic algorithms. New Generation Comput., 2011, Vol. 29 (3), pp. 309–327.
• [12] WOLOSZYNSKI T, KURZYŃSKI M. W., A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recognition, 2011, Vol. 44 (10-11), pp. 2656–2668.