SEKAID (Shannon Entropy Knowledge Acquisition in Insurance Domain) methodology for adaptive data mining analysis

Consultar RECERCAT

Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace: http://hdl.handle.net/2117/10014

Título:	SEKAID (Shannon Entropy Knowledge Acquisition in Insurance Domain) methodology for adaptive data mining analysis
Autor/a:	López Herrera, Josefina
Otros autores:	Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
Abstract:	SEKAID is a prediction methodology based on Shannon Entropy. From the analysis of clients’ insurance policy portfolios, it can be determined if the policy will be cancelled or not during a determined period of time. With the obtained results, marketing campaigns can be undertaken which allow the client loyalty of the portfolio to be improved. To demonstrate the effectiveness of this methodology, an automobile insurance portfolio is analyzed. A software application was developed to help perform the necessary tests in order to determine the most adequate configuration.
Abstract:	Peer Reviewed
Materia(s):	-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Simulation -Artificial intelligence -Data mining -Insurance -Simulació, Mètodes de -Intel·ligència artificial -Mineria de dades
Derechos:
Tipo de documento:	Artículo - Versión publicada Objeto de conferencia
Editor:	Academic Press
Compartir:

López Herrera, Josefina; Cellier, François E.; Cembrano Gennari, Gabriela

Coordinación

Patrocinio