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Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm (CROSBI ID 58342)

Prilog u knjizi | izvorni znanstveni rad

Klepac, Goran ; Kopal, Robert ; Mršić, Leo Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm // Analyzing Risk through Probabilistic Modeling in Operations Research / Dariusz Jacek Jakóbczak (Technical University of Koszalin, Poland) (ur.).: IGI Global, 2016. str. 91-124

Podaci o odgovornosti

Klepac, Goran ; Kopal, Robert ; Mršić, Leo

engleski

Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm

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).

Risk Profiling , Bayesian Networks

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Podaci o prilogu

91-124.

objavljeno

Podaci o knjizi

Analyzing Risk through Probabilistic Modeling in Operations Research

Dariusz Jacek Jakóbczak (Technical University of Koszalin, Poland)

IGI Global

2016.

9781466694583

Povezanost rada

Informacijske i komunikacijske znanosti, Računarstvo