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).

Customer Profiling in Complex Analytical Environments Using Swarm Intelligence Algorithms

“…A predictive model which calculates probability of buying for many products, are too complex as a tool for profiling, because there is to many combinatory states for precise profiling. Hypothetically, it could be used for this purpose, but it is not convenient for practical business purposes (Klepac, 2016).…”

Particle Swarm Optimization Algorithm as a Tool for Profile Optimization

“…In case that predictive model exists, it can be used as a base for diversification. Particle swarm optimization algorithm has a potential for diversification tasks (Cheng, 2013;Kress, 2010;Klepac, 2015a;Klepac, 2016). This idea leads us to the concept presented in this chapter, in which the particle swarm optimization algorithm became a tool for diversification (profiling) based on the predictive model constructed using a neural network.…”