Probability estimation by an adapted genetic algorithm in web insurance

Bedenel, Anne-Lise; Jourdan, Laetitia; Biernacki, Christophe

Type de document :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès sans actes

URL permanente :

http://hdl.handle.net/20.500.12210/29303

Titre :

Probability estimation by an adapted genetic algorithm in web insurance

Auteur(s) :

Bedenel, Anne-Lise [Auteur]
Jourdan, Laetitia [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Biernacki, Christophe [Auteur]

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) :

WEB
BIC
Insurance
Genetic Algorithms

Discipline(s) HAL :

Informatique [cs]/Recherche opérationnelle [cs.RO]

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

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

CNRS
Centrale Lille
Université de Lille

Collections :

METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2020-06-08T14:10:57Z
2020-06-09T09:27:27Z

Fichiers

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Numéro de version	Lien	Date de modification
2	20.500.12210/29303*	2020-06-09T09:26:49Z
1	20.500.12210/29303.1	2020-06-08T14:10:57Z

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Probability estimation by an adapted genetic ...

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Excel

RIS