In Extraction et gestion des connaissances, EGC 2022, blois, france, 24 au 28 janvier 2022
Abstract While existing GNN’s explanation methods explain the decision by studying the output layer, we propose a method that analyzes the hidden layers to identify the neurons that are co-activated for a class. We associate to them a graph.
In Applied Energy
Abstract The price of electricity on the European market is very volatile. This is due both to its mode of production by different sources, each with its own constraints (volume of production, dependence on the weather, or production inertia), and by the difficulty of its storage. Being able to predict the prices of the next day is an important issue, to allow the development of intelligent uses of electricity. In this article, we investigate the capabilities of different machine learning techniques to accurately predict electricity prices.
In Extraction et gestion des connaissances, EGC 2022, blois, france, 24 au 28 janvier 2022
Abstract The need for predictive maintenance comes with an increasing number of incidents, where it is imperative to quickly decide which service to contact for corrective actions. Several predictive models have been designed to automate this process, but the efficient models are opaque (say, black boxes). Many approaches have been proposed to locally explain each prediction of such models. However, providing an explanation for every result is not conceivable when it comes to a large number of daily predictions to analyze.
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