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一種通過節(jié)點序?qū)?yōu)進行貝葉斯網(wǎng)絡(luò)結(jié)構(gòu)學習的算法

劉彬,  王海羽,  孫美婷,  劉浩然,  劉永記,  張春蘭

劉彬, 王海羽, 孫美婷, 劉浩然, 劉永記, 張春蘭. 一種通過節(jié)點序?qū)?yōu)進行貝葉斯網(wǎng)絡(luò)結(jié)構(gòu)學習的算法[J]. 電子與信息學報, 2018, 40(5): 1234-1241. doi: 10.11999/JEIT170675
引用本文: 劉彬, 王海羽, 孫美婷, 劉浩然, 劉永記, 張春蘭. 一種通過節(jié)點序?qū)?yōu)進行貝葉斯網(wǎng)絡(luò)結(jié)構(gòu)學習的算法[J]. 電子與信息學報, 2018, 40(5): 1234-1241. doi: 10.11999/JEIT170675
LIU Bin, WANG Haiyu, SUN Meiting, LIU Haoran, IU Yongji, HANG Chunlan. Learning Bayesian Network Structure from Node Ordering Searching Optimal[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1234-1241. doi: 10.11999/JEIT170675
Citation: LIU Bin, WANG Haiyu, SUN Meiting, LIU Haoran, IU Yongji, HANG Chunlan. Learning Bayesian Network Structure from Node Ordering Searching Optimal[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1234-1241. doi: 10.11999/JEIT170675

一種通過節(jié)點序?qū)?yōu)進行貝葉斯網(wǎng)絡(luò)結(jié)構(gòu)學習的算法

doi: 10.11999/JEIT170675
基金項目: 

國家自然科學基金(51641609)

Learning Bayesian Network Structure from Node Ordering Searching Optimal

Funds: 

The National Natural Science Foundation of China (51641609)

  • 摘要: 針對K2算法過度依賴節(jié)點序，遺傳算法節(jié)點序?qū)?yōu)效率差的問題，該文提出一種直接對節(jié)點序進行評分搜索的貝葉斯結(jié)構(gòu)學習算法。該算法以K2算法為基礎(chǔ)，首先通過計算支撐樹權(quán)重矩陣，構(gòu)建能夠定量評價節(jié)點序的適應(yīng)度函數(shù)。然后通過提出混合交叉策略和孤立節(jié)點處理機制，同時利用動態(tài)學習因子和倒置變異策略，提升遺傳算法節(jié)點序?qū)?yōu)的性能。最后將得到的節(jié)點序作為K2算法的先驗知識得到最優(yōu)貝葉斯網(wǎng)絡(luò)結(jié)構(gòu)。仿真結(jié)果表明，該方法解決了K2算法依賴先驗知識的問題，相比于其它優(yōu)化算法，評分值平均增加了13.11%。
    Abstract: The performance of the K2 algorithm depends on node ordering heavily, and the genetic algorithm can not find the node ordering effectively. For these problems, a new Bayesian structure learning algorithm, named NOK2 (Node Ordering searching for K2 algorithm), is proposed to solve the Bayesian structure learning problem by searching node ordering directly. According to the requirements of K2 algorithm based on prior knowledge and the weight matrix of spanning tree, the fitness function for quantitative evaluation of node ordering is established. The genetic algorithm is redesigned by a new method combines the dynamic learning constants, the hybrid crossover strategy, the inverted mutation strategy and the isolated node processing, so that the algorithm can find the node order of the highest fitness value, and this node sequence is taken as a prior knowledge of the K2 algorithm to obtain the optimal Bayesian network structure. Compared with other optimization algorithms, experimental results indicate that the NOK2 algorithm can significantly increase nearly 13.11% in the scoring metric values.
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出版歷程
  • 收稿日期:  2017-07-07
  • 修回日期:  2017-11-29
  • 刊出日期:  2018-05-19

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