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                    基于雙尺度約束模型的BN結構自適應學習算法

                    戴晶幗 任佳 董超 杜文才

                    戴晶幗, 任佳, 董超, 杜文才. 基于雙尺度約束模型的BN結構自適應學習算法. 自動化學報, 2021, 47(8): 1988-2001 doi: 10.16383/j.aas.c180226
                    引用本文: 戴晶幗, 任佳, 董超, 杜文才. 基于雙尺度約束模型的BN結構自適應學習算法. 自動化學報, 2021, 47(8): 1988-2001 doi: 10.16383/j.aas.c180226
                    Dai Jing-Guo, Ren Jia, Dong Chao, Du Wen-Cai. BN structure adaptive learning algorithm based on dual-scale constraint model. Acta Automatica Sinica, 2021, 47(8): 1988-2001 doi: 10.16383/j.aas.c180226
                    Citation: Dai Jing-Guo, Ren Jia, Dong Chao, Du Wen-Cai. BN structure adaptive learning algorithm based on dual-scale constraint model. Acta Automatica Sinica, 2021, 47(8): 1988-2001 doi: 10.16383/j.aas.c180226

                    基于雙尺度約束模型的BN結構自適應學習算法

                    doi: 10.16383/j.aas.c180226
                    基金項目: 

                    國家國際科技合作專項 2015DFR10510

                    國家自然科學基金 61562018

                    國家海洋局南海維權技術與重點實驗室開放基金 1704

                    ??谑兄攸c科技計劃項目 2017041

                    詳細信息
                      作者簡介:

                      戴晶幗 ?? 海南大學信息科學技術學院博士研究生. 主要研究方向為貝葉斯網絡, 智能優化.E-mail: djgolivia_edu@126.com

                      董超 ?? 國家海洋局南海調查技術中心副研究員. 主要研究方向為智能控制.E-mail: dongchaoxj888@126.com

                      杜文才 ?? 中國澳門城市大學數據科學研究院教授, 海南大學信息科學技術學院教授. 主要研究方向為數據挖掘, 物聯網技術. E-mail: wencai@hainu.edu.cn

                      通訊作者:

                      任佳    海南大學信息科學技術學院教授. 主要研究方向為智能控制, 機器學習. 本文通信作者.E-mail: renjia@hainu.edu.cn

                    • 本文責任編委 朱軍

                    BN Structure Adaptive Learning Algorithm Based on Dual-scale Constraint Model

                    Funds: 

                    International Science and Technology Cooperation Projects of China 2015DFR10510

                    National Natural Science Foundation of China 61562018

                    Open Foundation of Key Laboratory of Technology and Application for Safeguarding of Marine Rights and Interests 1704

                    Key Science and Technology Projects of Haikou, Hainan Province 2017041

                    More Information
                      Author Bio:

                      DAI Jing-Guo ?? Ph. D. candidate at the College of Infomation and Technology, Hainan University. Her research interest covers Bayesian network and intelligent optimization

                      DONG Chao ?? Associate professor at South China Sea Marine Engineering surveying Center of State Oceanic Administrtion. His main research interest is intelligent control

                      DU Wen-Cai ?? Professor at the Institute of Data Science, City University of Macau, China and the College of Infomation and Technology, Hainan University. His research interest covers data mining and internet of things

                      Corresponding author: REN Jia    Professor at the College of Infomation and Technology, Hainan University. His research interest covers intelligent control and machine learning. Corresponding author of this paper
                    • Recommended by Associate Editor ZHU Jun
                    • 摘要: 在無先驗信息的情況下, 貝葉斯網絡(Bayesian network, BN)結構搜索空間的規模隨節點數目增加呈指數級增長, 造成BN結構學習難度急劇增加. 針對該問題, 提出基于雙尺度約束模型的BN結構自適應學習算法. 該算法利用最大互信息和條件獨立性測試構建大尺度約束模型, 完成BN結構搜索空間的初始化. 在此基礎上設計改進遺傳算法, 在結構迭代優化過程中引入小尺度約束模型, 實現結構搜索空間小尺度動態縮放. 同時, 在改進遺傳算法中構建變異概率自適應調節函數, 以降低結構學習過程陷入局部最優解的概率. 仿真結果表明, 提出的基于雙尺度約束模型的BN結構自適應學習算法能夠在無先驗信息的情況下保證BN結構學習的精度和迭代尋優的收斂速度.
                      Recommended by Associate Editor ZHU Jun
                      1)  本文責任編委 朱軍
                    • 圖  1  DSC-AL算法框架示意圖

                      Fig.  1  The framework of DSC-AL algorithm

                      圖  2  小尺度約束模型工作原理

                      Fig.  2  The working principle of small-scale constraint model

                      圖  3  DSC-AL算法流程圖

                      Fig.  3  The flowchart of DSC-AL algorithm

                      圖  4  節點順序交叉方法

                      Fig.  4  The crossover of node order

                      圖  5  三種標準BN結構示意

                      Fig.  5  Three benchmark BNs

                      圖  6  6種算法在ASIA-1000數據集下的3種性能指標的誤差條形圖

                      Fig.  6  Error bar graph of 3 measures for 6 algorithms on ASIA-1000 data set

                      圖  7  ASIA-1000下最優結構BIC評分平均值變化曲線

                      Fig.  7  The curves of BIC scores for optimal structures on ASIA-1000 data set

                      圖  8  ASIA-1000下優于上一代種群的個體數平均值變化曲線

                      Fig.  8  The curves of number of better individuals on ASIA-1000 data set

                      圖  9  6種算法在CAR_DIAGNOSIS2-2000數據集下的3種性能指標的誤差條形圖

                      Fig.  9  Error bar graph of 3 measures for 6 algorithms on CAR_DIAGNOSIS2-2000 data set

                      圖  10  CAR_DIAGNOSIS2-2000下最優結構BIC評分平均值變化曲線

                      Fig.  10  The curves of BIC scores for optimal structures on CAR_DIAGNOSIS2-2000 data set

                      圖  11  CAR_DIAGNOSIS2-2000下優于上一代種群的個體數平均值變化曲線

                      Fig.  11  The curves of number of better individuals on CAR_DIAGNOSIS2-2000 data set

                      圖  12  ALARM-2000下最優結構BIC評分平均值變化曲線

                      Fig.  12  The curves of BIC scores for optimal structures on ALARM-2000 data set

                      圖  13  ALARM-5000下最優結構BIC評分平均值變化曲線

                      Fig.  13  The curves of BIC scores for optimal structures on ALARM-5000 data set

                      表  1  ASIA模型下不同算法結果對比

                      Table  1  Comparisons of different methods on ASIA network

                      數據集 算法 IBIC BIC SHD RT BG
                      ASIA-1 000 (?2 325.3) DSC-AL ?2 375.1 (3.6570) ?2 320.5 (2.1782) 1.3667 (0.7184) 103.4270 (17.5317) 29.6667 (25.1812)
                      DGA ?2 406.9 (15.1353) ?2 329.5 (6.8571) 4.9333 (1.2576) 173.9571 (7.9109) 47.2333 (42.1775)
                      K2 / ?2 342.1 (14.0940) 7.5667 (2.1284) / /
                      DSC-AL + RdInit ?2 421.9 (19.5248) ?2 324.7 (4.7155) 3.8333 (2.0186) 104.3722 (23.3174) 44.7000 (49.7165)
                      DSC-AL + FixAlp ?2 372.3 (0.2821) ?2 320.2 (1.7524) 1.4333 (0.9353) 62.6387 (9.6306) 28.6333 (20.8450)
                      DSC-AL + RdAlp ?2 374.4 (2.7308) ?2 321.7 (3.3730) 2.1667 (1.7237) 85.2060 (7.6515) 39.1000 (28.7250)
                      DSC-AL + FixP ?2 374.8 (2.9988) ?2 322.0 (3.2387) 2.4000 (1.7927) 68.6206 (12.4026) 47.2667 (51.1205)
                      下載: 導出CSV

                      表  2  CAR DIAGNOSIS2模型下不同算法結果對比

                      Table  2  Comparisons of different methods on CAR DIAGNOSIS2 network

                      數據集 算法 IBIC BIC SHD RT BG
                      CAR DIAGNOSIS2-2000 (?11 922) DSC-AL ?13 865 (186.3612) ?11 774 (43.2254) 6.8000 (1.1861) 520.6599 (74.8401) 144.0667 (45.2601)
                      DGA ?15 546 (271.5482) ?11 795 (51.1551) 13.2000 (1.7301) 856.7351 (85.2662) 222.7667 (21.2630)
                      K2 / ?12 111 (198.0365) 23.5667 (5.4752) / /
                      DSC-AL + RdInit ?15 661 (415.5809) ?12 034 (181.5865) 13.8333 (3.0181) 508.8949 (67.7425) 194.5000 (67.9111)
                      DSC-AL + FixAlp ?13 557 (87.5065) ?11 745 (22.6139) 10.7000 (3.0867) 583.9935 (9.6306) 226.6667 (33.6988)
                      DSC-AL + RdAlp ?13 883 (177.8057) ?11 820 (37.1534) 9.9000 (2.0060) 426.4885 (63.1594) 172.5667 (57.0485)
                      DSC-AL + FixP ?13 860 (143.4086) ?11 825 (41.7158) 9.8667 (2.2242) 364.3424 (90.1956) 159.2667 (42.1303)
                      下載: 導出CSV

                      表  3  ALARM模型下不同算法結果對比

                      Table  3  Comparisons of different methods on ALARM network

                      數據集 算法 SHD RT BG
                      ALARM-2000 (?20 294) DSC-AL 15.1000 (2.7669) 2 898.8 (267.3125) 225.8000 (95.5671)
                      DGA 33.5000 (3.5071) 2 910.5 (122.4261) 498.1667 (1.4720)
                      BNC-PSO 25.3333 (5.5000) 2 689.1 (153.1974) 267.7778 (63.5227)
                      ALARM-5000 (?48 724) DSC-AL 13.5000 (0.9718) 2 322.7 (106.2002) 203.4000 (85.6364)
                      DGA 28.6667 (1.2111) 2 435.5 (239.3540) 498.3333 (3.1411)
                      BNC-PSO 16.3000 (3.6833) 1616.3 (473.0926) 315.9000 (98.0583)
                      下載: 導出CSV
                      360彩票
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                    • 收稿日期:  2018-04-17
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