A hybrid machine learning system and its application to insurance underwriting

Author

Nikolopoulos, Christos ; Duvendack, Shannon

Author_Institution

Dept. of Comput. Sci., Bradley Univ., Peoria, IL, USA

fYear

1994

fDate

27-29 Jun 1994

Firstpage

692

Abstract

This paper describes the application of evolutionary learning and classification tree techniques to the insurance underwriting domain. These machine learning techniques are used to build a knowledge base of rules for an expert system which determines when an insurance policy should be terminated. The effectiveness of each method is compared with the other and a hybrid method is proposed, which combines both approaches and seems to overshadow the performance of any other single method

Keywords

expert systems; genetic algorithms; insurance data processing; learning (artificial intelligence); learning systems; trees (mathematics); classification tree; evolutionary learning; expert system; genetic algorithm; hybrid machine learning system; hybrid method; insurance policy; insurance underwriting; knowledge base; machine learning techniques; rules; Classification tree analysis; Data mining; Expert systems; Genetic algorithms; Insurance; Knowledge acquisition; Knowledge engineering; Knowledge representation; Learning systems; Machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1899-4

Type

conf

DOI

10.1109/ICEC.1994.349974

Filename

349974