Context-based unsupervised ensemble learning and feature ranking

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In ensemble systems, several experts, which may have access to possibly different data, make decisions which are then fused by a combiner (meta-learner) to obtain a final result. Such ensemble-based systems are well-suited for processing big-data from sources such as social media, in-stream monitoring systems, networks, and markets, and provide more accurate results than single expert systems. However, most existing ensemble-learning techniques have two limitations: (i) they are supervised, and hence they require access to the true label, which is often unknown in practice, and (ii) they are not able to evaluate the impact of the various data features/contexts on the final decision, and hence they do not learn which data is required. In this paper we propose a joint estimation–detection method for evaluating the accuracy of each expert as a function of the data features/context and for fusing the experts decisions. The proposed method is unsupervised: the true labels are not available and no prior information is assumed regarding the performance of each expert. Extensive simulation results show the improvement of the proposed method as compared to the state-of-the-art approaches. We also provide a systematic, unsupervised method for ranking the informativeness of each feature on the decision making process.

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Machine Learning

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