Titre :

Estimation de probabilités par un algorithme génétique adapté.

Auteur(s) :

Bedenel, Anne-Lise [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]
Jourdan, Laetitia [Auteur]
Université de Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Operational Research, Knowledge And Data [ORKAD]
Biernacki, Christophe [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Université de Lille

Titre de la manifestation scientifique :

LION 12 - Learning and Intelligent Optimization Conference

Ville :

Kalamata

Pays :

Grèce

Date de début de la manifestation scientifique :

2018-06-10

Mot(s)-clé(s) en anglais :

Insurance
Genetic Algorithms
BIC
WEB

Discipline(s) HAL :

Computer Science [cs]/Operations Research [math.OC]
Mathématiques [math]/Statistiques [math.ST]
Mathématiques [math]/Probabilités [math.PR]

Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL