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This paper proposes a method for classification of incomplete data using neural network ensembles. In the method, the incomplete data set is analyzed and projected into a group of complete data subsets that give a full description of the known values in the data set by joining together. Those complete data subsets are then used as the training sets for the neural networks. Base classifiers are selected...
The importance of incremental learning in changing environments has been acknowledged in recent years. In this paper we present an ensemble learning method for supervised learning with drifting concepts. The method employs hypothesis test as mechanism for detecting concept drift and learns a base classifier for each new training data chunk. Former classifiers deemed as usable by the hypothesis test...
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