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Multi-modal sensor fusion recently became a widespread technique to provide pervasive services with context-recognition capabilities. However, classifiers commonly used to implement this technique are still far from being perfect. Thus, fusion algorithms able to deal with significant inaccuracies are required. In this paper, the authors present preliminary results obtained with a novel approach that combines diverse classifiers through commonsense reasoning. The approach maps classification labels produced by classifiers to concepts organized within the ConceptNet network. Then it verifies their semantic proximity by implementing a greedy sub-graph search algorithm.
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