The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm
Abstract
Chapter introduce usage of particle swarm optimization algorithm and explained methodology, as a tool for discovering customer profiles based on previously developed Bayesian network (BN). Bayesian network usage is common known method for risk modelling although BN's are not pure statistical predictive models (like neural networks or logistic regression, for example) because their structure could also depend on expert knowledge. Bayesian network structure could be trained using algorithm but, from perspective of businesses requirements model efficiency and overall performance, it is recommended that domain expert modify Bayesian network structure using expert knowledge and experience. Chapter will also explain methodology of using particle swarm optimization algorithm as a tool for finding most riskiness profiles based on previously developed Bayesian network. Presented methodology has significant practical value in all phases of decision support in business environment (especially for complex environments).
Related Content
Jun Sung Hong, Alberto Valido, Luz E. Robinson.
© 2024.
26 pages.
|
Adrijana GrmuĊĦa, Jun Sung Hong.
© 2024.
48 pages.
|
Justin J. Joseph, N. Alexander Aguado, Christoper W. Purser.
© 2024.
30 pages.
|
Sivani Pegadraju, Zidan Kachhi.
© 2024.
26 pages.
|
Ramona Sue McNeal, Susan M. Kunkle, Lisa Dotterweich Bryan, Mary Schmeida.
© 2024.
24 pages.
|
Angela R. Staton, Tammy Gilligan, Michele Kielty.
© 2024.
22 pages.
|
Ranjit Singha, Surjit Singha, Alphonsa Diana Haokip, Shruti Jose, V. Muthu Ruben.
© 2024.
14 pages.
|
|
|