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Performance of classifcation confdence measures in dynamic classifer systems
Neural Network World
Classifier combining is a popular technique for improving classification quality. Common methods for classifier combining can be further improved by using dynamic classification confidence measures which adapt to the currently classified pattern. However, in the case of dynamic classifier systems, the classification confidence measures need to be studied in a broader context -as we show in this paper, the degree of consensus of the whole classifier team plays a key role in the process. Wedoi:10.14311/nnw.2013.23.019 fatcat:be5732ggnzflpefw2rdml7uuoa