Evolutionary optimization of fuzzy decision systems for automated insurance underwriting

Author

Bonisone, P.P. ; Subbu, Raj ; Aggour, Kareem S.

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1003

Lastpage

1008

Abstract

A robust method for automating the tuning and maintenance of fuzzy decision-making systems is presented. A configurable multi-stage mutation-based evolutionary algorithm optimally tunes the decision thresholds and internal parameters of fuzzy rule-based and case-based systems that decide the risk categories of insurance applications. The tunable parameters have a critical impact on the coverage and accuracy of. decision-making, and a reliable method to optimally tune these parameters is critical to the quality of decision-making and maintainability of these systems

Keywords

case-based reasoning; genetic algorithms; insurance data processing; knowledge based systems; automated insurance underwriting; case-based systems; configurable multi-stage mutation-based evolutionary algorithm; decision thresholds; evolutionary optimization; fuzzy decision systems; fuzzy decision-making systems; risk categories; robust method; rule-based systems; Automation; Decision making; Evolutionary computation; Fuzzy systems; Humans; Information technology; Insurance; Maintenance; Robustness; Throughput;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7280-8

Type

conf

DOI

10.1109/FUZZ.2002.1006641

Filename

1006641