A formalism is proposed for representing uncertain information on set-valued variables using the formalism of belief functions. A set-valued variable X on a domain Ω is a variable taking zero, one or several values in Ω. While defining mass functions on the frame 2 2 Ω is usually not feasible because of the double-exponential complexity involved, we propose an approach based on a definition of a restricted family of subsets of 2 Ω that is closed under intersection and has a lattice structure. Using recent results about belief functions on lattices, we show that most notions from Dempster-Shafer theory can be transposed to that particular lattice, making it possible to express rich knowledge about X with only limited additional complexity as compared to the single-valued case. An application to multi-label classification (in which each learning instance can belong to several classes simultaneously) is demonstrated.
Electric vehicles are currently a field of research with many challenges that raise the interest of many researchers. The major challenge is about the autonomy of electric vehicles, which is limited as compared to that of conventional vehicles, and thus the drivers' anxiety about reaching or not the desired destination is very important. In this paper, we propose an experimental study to understand the energy consumption of electric vehicles, and we investigate some factors that have an important impact on their autonomy, such as the route type, the driving style and the ambient temperature. This study can be very useful in a further step to conceive a driving assistance system that indicates in real time the remaining energy and gives online instructions to reach the next charging station or the final destination. The analysis reported in this paper is based on real-world data collected using a full electric car with different driving conditions.
Multi-label learning is increasingly required by many applications where instances may belong to several classes at the same time. In this paper, we propose a fuzzy k-nearest neighbor method for multi-label classification using the veristic variable framework. Veristic variables are variables that can assume simultaneously multiple values with different degrees. In multi-label learning, class labels can be considered as veristic variables since each instance can belong simultaneously to more than one class. Several applications on benchmark datasets demonstrate the efficiency of our approach.
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