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.

Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models

2015 ◽  
pp. 406-434 ◽  
Author(s):  
Goran Klepac
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Management ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimal Values ◽  
Input Variables ◽  
Predictive Data Mining ◽  
Customer Profiles

This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed predictive model that predicts events like selection of some products or services with some probabilities. Particle swarm optimization algorithm is used as a tool that finds optimal values of input variables within developed predictive models as referent values for maximization value of probability that customers select/buy a product or service. Recognized results are used as a base for finding similar profiles between customers. The presented methodology has practical value for decision support in business, where information about customer profiles are valuable information for campaign planning and customer portfolio management.

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).

A Robust Cooperative Coevolutionary Particle Swarm Optimization Algorithm for Triangulation of Bayesian Networks

2011 ◽  
Vol 181-182 ◽  
pp. 468-473
Author(s):  
Xu Chu Dong ◽  
Dan Tong Ouyang ◽  
Dian Bo Cai ◽  
Yu Xin Ye ◽  
Sha Sha Feng
Keyword(s):  
Particle Swarm Optimization ◽  
Bayesian Networks ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Small Scale ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

In this paper, a cooperative coevoluationary particle swarm optimization algorithm, CCMDPSO, is proposed to solve the optimization problem of triangulation of Bayesian networks. It arranges all the variables of a given Bayesian network into some groups according to the global best solution and performs optimization on these small-scale groups. The basic optimizer of CCMDPSO is an improved discrete particle swarm optimization algorithm, MDPSO. Experiments show that CCMDPSO is an effective and robust method for the triangulation problem.

Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models

2016 ◽  
pp. 864-892
Author(s):  
Goran Klepac
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Management ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimal Values ◽  
Input Variables ◽  
Predictive Data Mining ◽  
Customer Profiles

This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed predictive model that predicts events like selection of some products or services with some probabilities. Particle swarm optimization algorithm is used as a tool that finds optimal values of input variables within developed predictive models as referent values for maximization value of probability that customers select/buy a product or service. Recognized results are used as a base for finding similar profiles between customers. The presented methodology has practical value for decision support in business, where information about customer profiles are valuable information for campaign planning and customer portfolio management.

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).

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