New untrained aggregation methods for classifier combination

The combined classification is a promising direction in pattern recognition and there are numerous methods that deal with forming classifier ensembles. The most popular approaches employ voting, where the final decision of compound classifier is a combination of individual classifiers´ outputs, i.e., class labels or support functions. This paper concentrates on the problem how to design an effective combination rule, which takes into consideration the values of support functions returned by the individual classifiers. Because in many practical tasks we do not have a training set at our disposal, then we express our interest in aggregation methods which do not require learning. A special attention is paid to weighted aggregation, especially when the different weights depend on particular support function of a given individual classifier. We propose a novel approach for untrained combination of support functions using the Gaussian function to assign mentioned above weights. The computer experiments carried out on the set of benchmark data sets confirm the advantages of the proposed approach for particular cases, especially when the number of class labels is high.