Estimation de probabilités par un algorithme ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Estimation de probabilités par un algorithme génétique adapté.
Author(s) :
Bedenel, Anne-Lise [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Jourdan, Laetitia [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille
Biernacki, Christophe [Auteur]
Université de Lille
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Jourdan, Laetitia [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille
Biernacki, Christophe [Auteur]
Université de Lille
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Conference title :
LION 12 - Learning and Intelligent Optimization Conference
City :
Kalamata
Country :
Grèce
Start date of the conference :
2018-06-10
English keyword(s) :
Insurance
Genetic Algorithms
BIC
WEB
Genetic Algorithms
BIC
WEB
HAL domain(s) :
Computer Science [cs]/Operations Research [math.OC]
Mathématiques [math]/Statistiques [math.ST]
Mathématiques [math]/Probabilités [math.PR]
Mathématiques [math]/Statistiques [math.ST]
Mathématiques [math]/Probabilités [math.PR]
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :
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