A Fast Calculation of Metric Scores for Learning Bayesian Network

Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for freque...

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Bibliographic Details
Published inInternational journal of automation and computing Vol. 9; no. 1; pp. 37 - 44
Main Authors Lv, Qiang, Xia, Xiao-Yan, Qian, Pei-De
Format Journal Article
LanguageEnglish
Published Heidelberg Institute of Automation, Chinese Academy of Sciences 01.02.2012
Springer Nature B.V
Subjects
Algorithms
Bayesian analysis
CAE) and Design
Cognitive tasks
Computer Applications
Computer-Aided Engineering (CAD
Control
Counting
Empirical analysis
Engineering
Learning
Machine learning
Mathematical analysis
Mathematical models
Mechatronics
Networks
Robotics
State of the art
二级缓存
公制
分数
学习任务
快速计算方法
数字系统
机器学习算法
贝叶斯网络
Frequent counting
metric score
learning Bayesian network
radix-based calculation
ADtree
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Summary:Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.
Bibliography:Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.
11-5350/TP
Frequent counting, radix-based calculation, ADtree, learning Bayesian network, metric score
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1476-8186
2153-182X
1751-8520
2153-1838
DOI:10.1007/s11633-012-0614-8