Novel Bayesian Network Incremental Learning Method Based on Particle Swarm Optimization Algorithm

2020 ◽  
pp. 941-947
Author(s):  
Yunnan Ling ◽  
Neng Yang ◽  
Haitao Yu ◽  
Yungang Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Network ◽  
Optimization Algorithm ◽  
Incremental Learning ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Learning Method ◽  
Swarm Optimization ◽  
Incremental Learning Method

Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm

2016 ◽  
pp. 1580-1612
Author(s):  
Goran Klepac ◽  
Leo Mrsic ◽  
Robert Kopal
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Network ◽  
Network Structure ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Expert Knowledge ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Bayesian Network Structure ◽  
Risk Profiling

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).

Particle Swarm Optimization Algorithm as a Tool for Profile Optimization

2015 ◽  
Vol 5 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Goran Klepac
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Predictive Model ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Profile Optimization ◽  
Customer Profiles

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.

Efficient Risk Profiling Using Bayesian Networks and Particle Swarm Optimization Algorithm

2016 ◽  
pp. 91-124 ◽  
Author(s):  
Goran Klepac ◽  
Leo Mrsic ◽  
Robert Kopal
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Network ◽  
Network Structure ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Expert Knowledge ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Bayesian Network Structure ◽  
Risk Profiling

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).

Research on Classification of Privacy Protection Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 496-500 ◽  
pp. 1861-1864
Author(s):  
Yu Chen ◽  
Yuan Xin Tang ◽  
Zhou Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Network ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Structure Learning ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

In this paper, through the analysis of the characteristics of particle swarm optimization algorithm, combined with the specific circumstances of Bayesian network structure learning, proposed to based on improved particle swarm algorithm.The algorithm uses the BIC measure function as a standard Bayesian network, while preserving the optimal particle case, the possibility of a mutation operation is added to decrease the algorithm into a local optimum. Through a typical Asia network, show that the algorithm is feasible, and other related algorithm is better than the experiment, the effectiveness of the algorithm. In this paper, the algorithm is verified from two aspects of theory and experiments, the results show that the algorithm is feasible.

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