Fuzzy multi-label learning under veristic variables

International audienc

Contribution of different uncertainty theories to multi-label classification

COMPIEGNE-BU (601592101) / SudocSudocFranceF

Representing uncertainty on set-valued variables using belief functions

International audienceA 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

Evidential multi-Label classification approach to learning from data with imprecise labels

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A dependent multi-label classification method derived from the k-nearest neighbor rule

International audienceIn multi-label classification, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. The most commonly-used approach for multi-label classification is where a binary classifier is learned independently for each possible class. However, multi-labeled data generally exhibit relationships between labels, and this approach fails to take such relationships into account. In this paper, we describe an original method for multi-label classification problems derived from a Bayesian version of the k-Nearest Neighbor (k-NN) rule. The method developed here is an improvement on an existing method for multi-label classification, namely multi-label k-NN, which takes into account the dependencies between labels. Experiments on simulated and benchmark datasets show the usefulness and the efficiency of the proposed approach as compared to other existing methods

Analysis of the main factors influencing the energy consumption of electric vehicles

International audienceElectric vehicles are currently a held 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