BNC-PSO: structure learning of Bayesian networks by Particle Swarm Optimization

Gheisari, Soheila; Meybodi, M. R.

doi:10.1016/j.ins.2016.01.090

Cited by 92 publications

(34 citation statements)

References 37 publications

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“…In ALARM and INSURANCE, the convergence of ABC-BN was better than that of CMABC, which indicated that for the more complex Bayesian network structure, the convergence accuracy of ALARM was significantly improved when there was no significant difference in convergence speed between ALARM and ABC-BN. For a simpler To better illustrate the performance advantages of CMABC-BNL, the algorithm was compared with genetic algorithms (GA) [22], Bayesian network structure learning based on artificial bee colony algorithm (ABC-BN) [28], Bayesian network construction algorithm using PSO (BNC-PSO) [29] and GES [30] under 1000 data sets and 5000 data sets. The population of each algorithm was set to 50, and the maximum number of iterations was set to 100.…”

Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Hybrid Optimization Algorithm for Bayesian Network Structure Learning

et al. 2019

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Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effective Bayesian network structure is the foundation and core of the learning and application of Bayesian networks. In Bayesian network structure learning, the traditional method of utilizing expert knowledge to construct the network structure is gradually replaced by the data learning structure method. However, as a result of the large amount of possible network structures, the search space is too large. The method of Bayesian network learning through training data usually has the problems of low precision or high complexity, which make the structure of learning differ greatly from that of reality, which has a great influence on the reasoning and practical application of Bayesian networks. In order to solve this problem, a hybrid optimization artificial bee colony algorithm is discretized and applied to structure learning. A hybrid optimization technique for the Bayesian network structure learning method is proposed. Experimental simulation results show that the proposed hybrid optimization structure learning algorithm has better structure and better convergence.

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Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Hybrid Optimization Algorithm for Bayesian Network Structure Learning

et al. 2019

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“…This latter allows the demonstration of its relevance in learning the BN structure. Table 7 shows the experimental results generated by the algorithms IK2-BN, NDPSO-BN [ 19 ], ABC-B [ 40 ], BNC-PSO [ 20 ], SCA [ 41 ] and MMHC [ 42 ] for each database (repeated 500 times). This table illustrates the difference between the original network topology and the learned structure.…”

Section: Evaluation and Discussionmentioning

confidence: 99%

A Novel Decision-Making Process for COVID-19 Fighting Based on Association Rules and Bayesian Methods

Khediri

Thaljaoui

Alfayez

2021

The Computer Journal

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Since recording the first case in Wuhan in November 2020, COVID-19 is still spreading widely and rapidly affecting the health of millions all over the globe. For fighting against this pandemic, numerous strategies have been made, where the early isolation is considered among the most effective ones. Proposing useful methods to screen and diagnose the patient’s situation for the purpose of specifying the adequate clinical management represents a significant challenge in diminishing the rates of mortality. Inspired from this current global health situation, we introduce a new autonomous process of decision-making that consists of two modules. The first module is the data analysis based on Bayesian network that is employed to indicate the coronavirus symptoms severity and then classify COVID-19 cases as severe, moderate or mild. The second module represents the decision-making based on association rules method that generates autonomously the adequate decision. To construct the model of Bayesian network, we used an effective method-oriented data for the sake of learning its structure. As a result, the algorithm accuracy in making the correct decision is 30% and in making the adequate decision is 70%. These experimental results demonstrate the importance of the suggested methods for decision-making.

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“…Later on, optimization starts when particles pass on their next velocity and positions and fitness value is calculated. Fitness function differs depending on the related study (Gheisari and Meybodi, 2016). In Eq.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

An Ensemble Inertia Weight Calculation Strategy in Particle Swarm Optimization Algorithm

Aydilek¹

2018

Scitech

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The ultimate success of particle swarm optimization depends on the velocity values of previous particles. Velocity is multiplied with inertia weight coefficient, and has a significant effect on search capability of the particle swarm optimization. When looking at previous studies that are carried out to calculate this coefficient, it is seen that inertia weight coefficient has been handled in several ways. In this article; a new ensemble inertia weight calculation strategy is proposed that uses other constant, random, linear decreasing, global local best, simulated annealing and chaotic inertia weight calculation methods. Other methods results are combined and used to make a final output decision in a proper way. In experimental tests, 30 common optimization benchmark test problems are used. Proposed ensemble strategy is proven by statistical tests and gives successful results in all optimization benchmark test problems.

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BNC-PSO: structure learning of Bayesian networks by Particle Swarm Optimization

Cited by 92 publications

References 37 publications

Hybrid Optimization Algorithm for Bayesian Network Structure Learning

Hybrid Optimization Algorithm for Bayesian Network Structure Learning

A Novel Decision-Making Process for COVID-19 Fighting Based on Association Rules and Bayesian Methods

An Ensemble Inertia Weight Calculation Strategy in Particle Swarm Optimization Algorithm

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