Probability estimation by an adapted genetic algorithm in web insurance

Anne-Lise Bedenel 1, 2, 3 Laetitia Jourdan 4, 1, 5 Christophe Biernacki 2, 4
3 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille - École polytechnique universitaire de Lille
5 ORKAD - Operational Research, Knowledge And Data
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : In the insurance comparison domain, data constantly evolve, implying some difficulties to directly exploit them. Indeed, most of the classical learning methods require data descriptors equal to both learning and test samples. To answer business expectations, online forms where data come from are regularly modified. This constant modification of features and data descriptors makes statistical analysis more complex. A first work with statistical methods has been realized. This method relies on likelihood and models selection with the Bayesian information criterion. Unfortunately, this method is very expensive in computation time. Moreover, with this method, all models should be exhaustively compared, what is materially unattainable, so the search space is limited to a specific models family. In this work, we propose to use a genetic algorithm (GA) specifically adapted to overcome the statistical method defaults and shows its performances on real datasets provided by the company MeilleureAssur-ance.com.
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Anne-Lise Bedenel, Laetitia Jourdan, Christophe Biernacki. Probability estimation by an adapted genetic algorithm in web insurance. LION 12 - Learning and Intelligent Optimization Conference, Jun 2018, Kalamata, Greece. ⟨hal-01885117⟩

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