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In this paper, two new infrared precipitation estimation approaches based on the concept of k-means clustering are first proposed, named the NAW-Kmeans and the GPI-Kmeans methods. Then, they are adapted to the southern Mediterranean basin, where the subtropical climate prevails. The infrared data (10.8μm channel) acquired by MSG-SEVIRI sensor in winter and spring 2012 are used. Tests are carried out...
Efficient classification depends strongly on the quality of the dataset used in experiments. In this paper, we generated a dataset consists of six spectral features extracted from the MSG-SEVIRI satellite images. Each feature represents the brightness temperature of the corresponding pixel. We are based on meteorological radar images acquired in Sétif region (Algeria) to assign a class to each feature...
We propose a novel feature selection methodology based on game theory. In this context, the players are the various feature selection methods and the characteristic function (payoff) represents the feature ranking agreement within a coalition of players. The Shapley value assigned to each feature selection method is computed and ranked from higher to lower. The best feature selection method is identified...
We propose a new filter methodology for feature selection using the concept of game theory whereby features are assimilated to players. In this game theoretical context, a strategy corresponds to a particular affinity between a group of features forming a cluster, and the payoff function is computed based on the weighted distance between a feature and a cluster. A zero-sum two-player game problem...
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