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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%。
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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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