A BAYESIAN NETWORKS STRUCTURAL LEARNING ALGORITHM BASED ON A MULTIEXPERT APPROACH

We propose a new learning algorithm, structural learning with the complementary coding for concept learning problems. We introduce the new grouping measure that forms the similarity matrix over the training set and show this similarity matrix provides a sufficient condition for the linear separability of the set. Using the sufficient condition one should figure out a suitable composition of linearly separable threshold functions that classify exactly the set of labeled vectors. In the case of the nonlinear separability, the internal representation of connectionist networks, the number of the hidden units and value-space of these units, is pre-determined before learning based on the structure of the similarity matrix. A three-layer neural network is then constructed where each linearly separable threshold function is computed by a linear-threshold unit whose weights are determined by the one-shot learning algorithm that requires a single presentation of the training set. The structural learning algorithm proceeds to capture the connection weights so as to realize the pre-determined internal representation. The pre-structured internal representation, the activation value spaces at the hidden layer, defines intermediate-concepts. The target-concept is then learned as a combination of those intermediate-concepts. The ability to create the pre-structured internal representation based on the grouping measure distinguishes the structural learning from earlier methods such as backpropagation.

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An improved bayesian networks learning algorithm based on independence test and MDL scoring

Proceedings of the 2005 International Conference on Active Media Technology, 2005. (AMT 2005). ◽

10.1109/amt.2005.1505360 ◽

2005 ◽

Author(s):

Junzhong Ji ◽

Jing Yan ◽

Chunnian Liu ◽

Ning Zhong

Keyword(s):

Bayesian Networks ◽

Learning Algorithm ◽

Independence Test

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Learning Continuous Time Bayesian Networks in Non-stationary Domains

Journal of Artificial Intelligence Research ◽

10.1613/jair.5126 ◽

2016 ◽

Vol 57 ◽

pp. 1-37 ◽

Cited By ~ 2

Author(s):

Simone Villa ◽

Fabio Stella

Keyword(s):

Bayesian Networks ◽

Continuous Time ◽

Learning Algorithm ◽

State Of The Art ◽

Synthetic Data ◽

Score Function ◽

Dynamic Bayesian Networks ◽

Continuous Time Bayesian Networks ◽

Real World Datasets ◽

Transition Times

Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node to change over continuous time. Three settings are developed for learning non-stationary continuous time Bayesian networks from data: known transition times, known number of epochs and unknown number of epochs. A score function for each setting is derived and the corresponding learning algorithm is developed. A set of numerical experiments on synthetic data is used to compare the effectiveness of non-stationary continuous time Bayesian networks to that of non-stationary dynamic Bayesian networks. Furthermore, the performance achieved by non-stationary continuous time Bayesian networks is compared to that achieved by state-of-the-art algorithms on four real-world datasets, namely drosophila, saccharomyces cerevisiae, songbird and macroeconomics.

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Learning Bayesian networks based on bi-velocity discrete particle swarm optimization with mutation operator

Open Mathematics ◽

10.1515/math-2018-0086 ◽

2018 ◽

Vol 16 (1) ◽

pp. 1022-1036

Author(s):

Jingyun Wang ◽

Sanyang Liu

Keyword(s):

Particle Swarm Optimization ◽

Bayesian Networks ◽

Structure Learning ◽

Learning Algorithm ◽

Particle Swarm ◽

Mutation Operator ◽

Discrete Particle Swarm Optimization ◽

Swarm Optimization ◽

Discrete Particle ◽

Mutation Strategy

AbstractThe problem of structures learning in Bayesian networks is to discover a directed acyclic graph that in some sense is the best representation of the given database. Score-based learning algorithm is one of the important structure learning methods used to construct the Bayesian networks. These algorithms are implemented by using some heuristic search strategies to maximize the score of each candidate Bayesian network. In this paper, a bi-velocity discrete particle swarm optimization with mutation operator algorithm is proposed to learn Bayesian networks. The mutation strategy in proposed algorithm can efficiently prevent premature convergence and enhance the exploration capability of the population. We test the proposed algorithm on databases sampled from three well-known benchmark networks, and compare with other algorithms. The experimental results demonstrate the superiority of the proposed algorithm in learning Bayesian networks.

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A Bayesian network structural learning algorithm for calculating the failure probabilities of complex engineering systems with limited data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211354 ◽

2021 ◽

pp. 1-14

Author(s):

Yong Chen ◽

Tianbao Zhang ◽

Ruojun Wang ◽

Lei Cai

Keyword(s):

Bayesian Network ◽

Learning Algorithm ◽

Hill Climbing ◽

Structural Learning ◽

Engineering Systems ◽

Limited Data ◽

Model Complex ◽

Complex Engineering ◽

Complex Engineering Systems ◽

Three Components

The failure of complex engineering systems is easy to lead to disastrous consequences. To prevent the failure, it is necessary to model complex engineering systems using probabilistic techniques with limited data which is a major feature of complex engineering systems. It is a good choice to perform such modeling using Bayesian network because of its advantages in probabilistic modeling. However, few Bayesian network structural learning algorithms are designed for complex engineering systems with limited data. Therefore, an algorithm for learning the Bayesian network structure of them should be developed. Based on the process of self-purification of water, a complex engineering system is segmented into three components according to the degree of difficulty in solving them. And then a Bayesian network learning algorithm with three components (TC), including PC algorithm, MIK algorithm which is originated by the paper through combining Mutual Information and K2 algorithm, and the Hill-Climbing method, is developed, i.e. TC algorithm. To verify its effectiveness, TC algorithm, K2 algorithm, and Max-Min Hill-Climbing are respectively used to learn Alarm network with different sizes of samples. The results imply that TC algorithm has the best performance. Finally, TC algorithm is applied to study tank spill accidents with 220 samples.

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