Method of Static Classifiers Selection Using the Weights of Base Classifiers
The choice of a pertinent objective function is one of the most crucial elements in static ensemble selection. In this study, a new approach of calculating the weight of base classifiers is developed. The values of these weights are the basis for the selection process of classifiers from the initial pool. The obtained weights are interpreted in the context of the interval logic. A number of experiments have been carried out on several datasets available in the UCI repository. The performed experiments compare the proposed algorithms with base classifiers, oracle, sum, product, and mean methods.
KeywordsClassifier fusion Interval logic Static classifiers selection Multiple classifier system
This work was supported by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264 and by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Technology.
- 1.Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Secaucus (2006)Google Scholar
- 4.Didaci, L., Giacinto, G., Roli, F., Marciali, G.L.: A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognition, 28, 2188–2191, 11/2005 (2005)Google Scholar
- 5.dos Santos, E.M., Sabourin, R.: Classifier ensembles optimization guided by population oracle. In: IEEE Congress on Evolutionary Computation, pp. 693–698 (2011)Google Scholar
- 6.Duin, R., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D., Tax, D., Verzakov. S.: PR-Tools4.1, A Matlab Toolbox for Pattern Recognition. Delft University of Technology (2007)Google Scholar
- 7.Frank, A., Asuncion, A.: UCI machine learning repository Irvine CA (2010) http://archive.ics.uci.edu/ml
- 12.Jackowski, K., Krawczyk, B., Woźniak, M.: Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning. Int. J. Neural Syst. 24(03) (2014)Google Scholar
- 16.Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley New York (2014)Google Scholar
- 19.Rejer, I.: Genetic algorithms in EEG feature selection for the classification of movements of the left and right hand. In: Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, pp. 579–589. Springer (2013)Google Scholar