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| Paper IPM / Cognitive / 11779 |
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In this paper, a method for handling imperfect labels using belief functions has been presented. By extracting different types of features from data, the proposed method takes advantage of information redundancy and complementariness between sources. The initial label of each training sample is ignored and based on its closeness to prototypes of the main classes, it is then reassigned to one class or any subset of the pre-defined classes. MLP neural network is used as base classifier and its outputs are interpreted as BBA and in this way, partial knowledge about the class of a test pattern is encoded. The BBAs are then discounted based on the reliability of the base classifiers in identifying validation samples and are pooled using Dempster's rule of combination. Experiments with artificial and real data demonstrate that by considering the ambiguity in labels of the learning data, the roposed method can outperform single and ensemble classifiers that solve the classification problem using data with initial imperfect labels.
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