Neural Networks in Cognitive Science

Yusoff, Nooraini; Sporea, Ioana; Grüning, André

doi:10.4018/978-1-61350-092-7.ch004

“…In general, it leads us to conclusion that instead of single profile monitoring a company can also monitor profile groups (Shi, 2012;Yusoff et al, 2012). Also, same as for single profiles, periodical processing on profile group level over time could give better and clearer picture about changes in risk profiles on recognized group level and could be used as a trigger for further market activities, as well as a generator of new ideas.…”

Section: Discussionmentioning

confidence: 99%

Particle Swarm Optimization Algorithm as a Tool for Profile Optimization

Klepac

¹

2015

International Journal of Natural Computing Research

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Complex analytical environment is challenging environment for finding customer profiles. In situation where predictive model exists like Bayesian networks challenge became even bigger regarding combinatory explosion. Complex analytical environment can be caused by multiple modality of output variable, fact that each node of Bayesian network can potetnitaly be target variable for profiling, as well as from big data environment, which cause data complexity in way of data quantity. As an illustration of presented concept particle swarm optimization algorithm will be used as a tool, which will find profiles from developed predictive model of Bayesian network. This paper will show how partical swarm optimization algorithm can be powerfull tool for finding optimal customer profiles given target conditions as evidences within Bayesian networks.

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Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm

Klepac

¹

,

Mršić²,

Kopal³

2016

Analyzing Risk Through Probabilistic Modeling in Operations Research

5

0

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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).

show abstract

Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm

Klepac

¹

,

Mršić²,

Kopal³

Psychology and Mental Health

0

View full text Add to dashboard Cite

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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Neural Networks in Cognitive Science

Cited by 4 publications

References 45 publications

Particle Swarm Optimization Algorithm as a Tool for Profile Optimization

Particle Swarm Optimization Algorithm as a Tool for Profile Optimization

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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