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Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm

Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm
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Author(s): Goran Klepac (Raiffeisenbank Austria d.d., Croatia), Leo Mrsic (IN2data Ltd. Data Science Company, Croatia) and Robert Kopal (IN2data Ltd. Data Science Company, Croatia)
Copyright: 2016
Pages: 34
Source title: Analyzing Risk through Probabilistic Modeling in Operations Research
Source Author(s)/Editor(s): Dariusz Jacek Jakóbczak (Technical University of Koszalin, Poland)
DOI: 10.4018/978-1-4666-9458-3.ch004

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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).

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