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Appariement de descripteurs évoluant en temps : Application à la comparaison d'assurance en ligne

Anne-Lise Bedenel 1 Christophe Biernacki 1 Laetitia Jourdan 2
1 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : In the web domain, and in particular for insurance comparison, data constantly evolve, implying that it is dicult to directly exploit them. For example, to do a classication, performing standard learning processes require data descriptor equal for both learning and test samples. Indeed, for answering to web surfer expectation, online forms whence data come from are regularly modied. So, features and data descriptors are also regularly modied. In this work, we introduce a process to estimate and understand connections between transformed data descriptors. This estimated matching between descriptors will be a preliminary step before applying later classical learning methods.
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Submitted on : Friday, October 14, 2016 - 4:36:06 PM
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Anne-Lise Bedenel, Christophe Biernacki, Laetitia Jourdan. Appariement de descripteurs évoluant en temps : Application à la comparaison d'assurance en ligne. 48èmes Journées des Statistiques Française, May 2016, Montpellier, France. ⟨hal-01381766⟩

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