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Traitement intelligent de données par réseaux de neurones artificiels : application à la valorisation des systèmes d'information géographiques

Abstract : The purpose of this thesis is: (i) establish predictive maps on ore deposits, (ii) select a subset of descriptive features that effectively contribute to the building of these predictive maps, (iii) identify and interpret dependencies between the selected features, (iv) place the features into a hierarchy that indicates their importance. A real-life data of Geographical Information System provided by the French geological survey (BRGM) have been used in the accomplished experiments. In order to establish predictive maps, we have used neural network ensemble which is a very successful technique where outputs of a set of separately trained neural network are combined to form one unified prediction. This technique generates several predictive maps following the used aggregation function. In addition, to understand domain data, we have focused on selecting a subset of relevant features. We have proposed an improvement of existing features selection techniques that are based on the principle of Optimal Brain Damage (OBD) as well as those of Optimal Brain Surgeon (OBS) and Mutual Information (MI). We have also proposed novel solutions to understand data that combine ensemble feature selection approach with either concept lattices or statistic techniques. The latter solutions help discovering all relevant features and organizing them into hierarchy according to their concurrencies in the selected subsets of features. Moreover, we have addressed the problem of clustering-based analysis of data provided with multiple labels. The proposed approach uses new measures that extend the scope of the recall and precision measures in information retrieval (IR) to the processing of multi-label data. Experiments have been carried out on data pertaining to geographical information system and documentary system have highlighted the accuracy of our approach for knowledge extraction.
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Submitted on : Friday, March 30, 2018 - 9:43:20 AM
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  • HAL Id : tel-01754272, version 1



Mohammed Attik. Traitement intelligent de données par réseaux de neurones artificiels : application à la valorisation des systèmes d'information géographiques. Autre [cs.OH]. Université Henri Poincaré - Nancy 1, 2006. Français. ⟨NNT : 2006NAN10211⟩. ⟨tel-01754272⟩



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