scholarly journals Learning Optimal Bayesian Networks: A Shortest Path Perspective

2013 ◽  
Vol 48 ◽  
pp. 23-65 ◽  
Author(s):  
C. Yuan ◽  
B. Malone
Keyword(s):  
Bayesian Network ◽  
Shortest Path ◽  
Network Structure ◽  
Search Algorithm ◽  
Scoring Function ◽  
Solution Space ◽  
Shortest Path Problem ◽  
Heuristic Function ◽  
Research Issues ◽  
Bayesian Network Structure

In this paper, learning a Bayesian network structure that optimizes a scoring function for a given dataset is viewed as a shortest path problem in an implicit state-space search graph. This perspective highlights the importance of two research issues: the development of search strategies for solving the shortest path problem, and the design of heuristic functions for guiding the search. This paper introduces several techniques for addressing the issues. One is an A* search algorithm that learns an optimal Bayesian network structure by only searching the most promising part of the solution space. The others are mainly two heuristic functions. The first heuristic function represents a simple relaxation of the acyclicity constraint of a Bayesian network. Although admissible and consistent, the heuristic may introduce too much relaxation and result in a loose bound. The second heuristic function reduces the amount of relaxation by avoiding directed cycles within some groups of variables. Empirical results show that these methods constitute a promising approach to learning optimal Bayesian network structures.

Evaluation of Bayesian Network Structure Learning Using Elephant Swarm Water Search Algorithm

2020 ◽  
pp. 139-159
Author(s):  
Shahab Wahhab Kareem ◽  
Mehmet Cudi Okur
Keyword(s):  
Simulated Annealing ◽  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Search Algorithm ◽  
Confusion Matrix ◽  
Data Sets ◽  
Greedy Search ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning which can represent probabilistic dependency relationships among the variables. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) for Bayesian network structure learning. In the algorithm; Deleting, Reversing, Inserting, and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly, water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with Pigeon Inspired Optimization, Simulated Annealing, Greedy Search, Hybrid Bee with Simulated Annealing, and Hybrid Bee with Greedy Search using BDeu score function as a metric for all algorithms. They investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of evaluations, the proposed algorithm achieves better performance than the other algorithms and produces better scores as well as the better values.

scholarly journals Heuristic Function Influence to the Global Optimum Value in Shortest Path Problem

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Shortest Path ◽  
Search Algorithm ◽  
Daily Life ◽  
Global Optimum ◽  
Shortest Path Problem ◽  
Manhattan Distance ◽  
Evaluation Function ◽  
Heuristic Function ◽  
Optimum Route

Determination of the optimum route is often encountered in daily life. The purpose of the optimum route itself is to find the best trajectory of the two pairs of vertices contained in a map or graph. The search algorithm applied is A*. This algorithm has the evaluation function to assist the search. The function is called heuristic. Two methods which have been introduced as a step to obtain the value of heuristic function are by using Euclidean and Manhattan distance. Both of these methods create the optimum distance in shortest path problem, but these functions gain the different results. This research performs the development of the heuristic function using Euclidean, Manhattan, Euclidean Square and the author method (Andysah).

scholarly journals A Hybrid Structure Learning Algorithm for Bayesian Network Using Experts’ Knowledge

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 620 ◽  
Author(s):  
Hongru Li ◽  
Huiping Guo
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Explicit Knowledge ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Scoring Function ◽  
Hybrid Structure ◽  
Learning Performance ◽  
Bayesian Network Structure ◽  
Using Data

Bayesian network structure learning from data has been proved to be a NP-hard (Non-deterministic Polynomial-hard) problem. An effective method of improving the accuracy of Bayesian network structure is using experts’ knowledge instead of only using data. Some experts’ knowledge (named here explicit knowledge) can make the causal relationship between nodes in Bayesian Networks (BN) structure clear, while the others (named here vague knowledge) cannot. In the previous algorithms for BN structure learning, only the explicit knowledge was used, but the vague knowledge, which was ignored, is also valuable and often exists in the real world. Therefore we propose a new method of using more comprehensive experts’ knowledge based on hybrid structure learning algorithm, a kind of two-stage algorithm. Two types of experts’ knowledge are defined and incorporated into the hybrid algorithm. We formulate rules to generate better initial network structure and improve the scoring function. Furthermore, we take expert level difference and opinion conflict into account. Experimental results show that our proposed method can improve the structure learning performance.

A Tag-Based Search Algorithm for Causal Bayesian Networks

2013 ◽  
Vol 756-759 ◽  
pp. 3103-3108
Author(s):  
Tian Ping Liu ◽  
Ming Ming Zhang ◽  
Yan Yang Wang
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Search Algorithm ◽  
Search Space ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning ◽  
Different Types ◽  
Causal Bayesian Networks ◽  
Causal Bayesian Network

In this study, in order to improve the search efficiency of causal Bayesian network structure learning, a new tag-based search algorithm is developed. The algorithm uses tags and the topology structure of tags to mark different types of variables, thus narrowing the search space of causal Bayesian network structure learning. With this algorithm, the task of combining causal Bayesian network theory with existing theories or models in certain application establishments when causal analysis is required becomes simpler. The time complexity of the tag-based search algorithm, compared with other search algorithms, has been reduced. Moreover, the experimental results show that the efficiency and accuracy of the tag-based search algorithm are both high.

scholarly journals An Algorithm Based on Loop Cutting Contribution Function for Loop Cutset Problem in Bayesian Network

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 462
Author(s):  
Jie Wei ◽  
Yufeng Nie ◽  
Wenxian Xie
Keyword(s):  
Bayesian Inference ◽  
Bayesian Network ◽  
Network Structure ◽  
Computational Efficiency ◽  
Numerical Experiments ◽  
Global Perspective ◽  
Node Pair ◽  
Bayesian Network Structure ◽  
Network Structure Analysis ◽  
Contribution Index

The loop cutset solving algorithm in the Bayesian network is particularly important for Bayesian inference. This paper proposes an algorithm for solving the approximate minimum loop cutset based on the loop cutting contribution index. Compared with the existing algorithms, the algorithm uses the loop cutting contribution index of nodes and node-pairs to analyze nodes from a global perspective, and select loop cutset candidates with node-pair as the unit. The algorithm uses the parameter μ to control the range of node pairs, and the parameter ω to control the selection conditions of the node pairs, so that the algorithm can adjust the parameters according to the size of the Bayesian networks, which ensures computational efficiency. The numerical experiments show that the calculation efficiency of the algorithm is significantly improved when it is consistent with the accuracy of the existing algorithm; the experiments also studied the influence of parameter settings on calculation efficiency using trend analysis and two-way analysis of variance. The loop cutset solving algorithm based on the loop cutting contribution index uses the node-pair as the unit to solve the loop cutset, which helps to improve the efficiency of Bayesian inference and Bayesian network structure analysis.

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