[1]曾繁慧,胡光闪,孙慧,等.因素空间理论下的因果概率推理分类算法研究[J].智能系统学报,2024,19(4):1042-1051.[doi:10.11992/tis.202206004]
ZENG Fanhui,HU Guangshan,SUN Hui,et al.A causal probabilistic inference classification algorithm based on factor space theory[J].CAAI Transactions on Intelligent Systems,2024,19(4):1042-1051.[doi:10.11992/tis.202206004]
点击复制
ZENG Fanhui,HU Guangshan,SUN Hui,et al.A causal probabilistic inference classification algorithm based on factor space theory[J].CAAI Transactions on Intelligent Systems,2024,19(4):1042-1051.[doi:10.11992/tis.202206004]
因素空间理论下的因果概率推理分类算法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第4期
页码:
1042-1051
栏目:
人工智能院长论坛
出版日期:
2024-07-05
- Title:
- A causal probabilistic inference classification algorithm based on factor space theory
- Keywords:
- factor space; causal probabilistic inference taxonomy; desirability taxonomy; Bayesian network; factor probability theory; conditional probability; causality; artificial intelligence
- 分类号:
- TP18
- 摘要:
- 机器学习方法与因果推理结合能极大地提升方法性能。为探究因果概率正逆向推理的分类效果，基于因素空间理论下的因素概率论，利用条件概率，研究正向因素概率推理原理及模型并提出正向因果概率推理分类法(forward causal probabilistic inference classification algorithm, FCPIC)和简化条件的可取度分类法；研究逆向因素概率推理原理及模型并结合贝叶斯网络提出逆向因果概率推理分类法(reverse causal probabilistic inference classification algorithm, RCPIC)。将3个分类算法与KNN（K-Nearest neighbor）和SVM（support vector machine）算法进行实例对比验证，研究结果表明：FCPIC算法、可取度分类算法和RCPIC算法简单有效、具有可行性和实用性，且可取度分类法和RCPIC算法性能优于SVM和KNN算法，FCPIC算法对实际数据预测中必要类有查全需求的情况更优。研究结论丰富了因素空间的理论研究和应用价值。
- Abstract:
- The integration of machine learning techniques with causal reasoning can significantly enhance method performance. To investigate the classification effect of positive and reverse causal probability inferences, we rely our study on factor probability theory under factor space theory. Using conditional probability, we examined the principles and model of positive-factor probabilistic reasoning. This led to the proposal of the forward causal probabilistic inference classification algorithm (FCPIC) and a desirability classification method of simplified conditions. We also explored the principles and model of inverse factor probabilistic inference, which resulted in the proposal of the reverse causal probabilistic inference classification algorithm (RCPIC) along with a Bayesian network. The three classification algorithms were compared with the K-nearest neighbor (KNN) and support vector machine (SVM) algorithms. The results demonstrate that the FCPIC algorithm, the desirability classification algorithm, and the RCPIC algorithm are simple, effective, feasible, and practical. The performance of the desirability classification method and RCPIC algorithm surpasses those of both SVM and KNN. Additionally, the FCPIC algorithm is better when dealing with cases where the necessary classes in actual data prediction have full demand. These research findings contribute to the theoretical research and application value of factor space.
- 参考文献/References:
-
[1] 汪培庄, SUGENO M. 因素场与模糊集的背景结构[J]. 模糊数学, 1982(2): 45–54
WANG Peizhuang, SUGENO M. The factors field and background structure for fuzzy subsets[J]. Fuzzy mathematics, 1982(2): 45–54
[2] 汪培庄, 李洪兴. 知识表示的数学理论[M]. 天津: 天津科学技术出版社, 1994.
[3] 李洪兴. 因素空间与模糊决策[J]. 北京师范大学学报(自然科学版), 1994, 30(1): 41–46
LI Hongxing. Factor spaces and fuzzy decision making[J]. Journal of Beijing Normal University (natural science edition), 1994, 30(1): 41–46
[4] 袁学海, 汪培庄. 因素空间和范畴[J]. 模糊系统与数学, 1995, 9(2): 25–33
YUAN Xuehai, WANG Peizhuang. Factor spaces and categories[J]. Fuzzy systems and mathematics, 1995, 9(2): 25–33
[5] WANG Peizhuang, LIU Zengliang, SHI Yong, et al. Factor space, the theoretical base of data science[J]. Annals of data science, 2014, 1(2): 233–251.
[6] 汪培庄, 郭嗣琮, 包研科, 等. 因素空间中的因素分析法[J]. 辽宁工程技术大学学报(自然科学版), 2014, 33(7): 865–870
WANG Peizhuang, GUO Sicong, BAO Yanke, et al. Causality analysis in factor spaces[J]. Journal of Liaoning Technical University (natural science edition), 2014, 33(7): 865–870
[7] 于福生, 罗承忠. 粒子因素空间与智能诊断专家系统[C]// 第七届全国电工数学学术年会论文集. 北京: 科学技术出版社, 1999: 24–27.
YU Fusheng, LUO Chengzhong. Granule factors space and intelligent diagnostic expert systems[C]//Proceedings of the 7th National Conference on Electric Mathematics. Beijing: China Science & Technology Press, 1999: 24-27.
[8] 刘海涛, 郭嗣琮. 因素分析法的推理模型[J]. 辽宁工程技术大学学报(自然科学版), 2015, 34(1): 124–128
LIU Haitao, GUO Sicong. Reasoning model of causality analysis[J]. Journal of Liaoning Technical University (natural science edition), 2015, 34(1): 124–128
[9] 曾繁慧, 李艺. 因素空间理论的决策树C4.5算法改进[J]. 辽宁工程技术大学学报(自然科学版), 2017, 36(1): 109–112
ZENG Fanhui, LI Yi. An improved decision tree algorithm based on factor space theory[J]. Journal of Liaoning Technical University (natural science edition), 2017, 36(1): 109–112
[10] 汪华东, 郭嗣琮. 基于因素空间反馈外延外包络的DFE决策[J]. 计算机工程与应用, 2015, 51(15): 148–152,156
WANG Huadong, GUO Sicong. DFE decision-making based on outer-enveloping of feedback extension in factor spaces[J]. Computer engineering and applications, 2015, 51(15): 148–152,156
[11] 孙慧, 曾繁慧, 蒲凌杰. 因素空间理论下多目标因果分析的降维算法[J]. 辽宁工程技术大学学报(自然科学版), 2021, 40(5): 466–472
SUN Hui, ZENG Fanhui, PU Lingjie. Research on dimension reduction algorithm of multiple targets causal analysis based on factor space theory[J]. Journal of Liaoning Technical University (natural science edition), 2021, 40(5): 466–472
[12] HELLSTR?M T. The relevance of causation in robotics: a review, categorization, and analysis[J]. Paladyn, journal of behavioral robotics, 2021, 12(1): 238–255.
[13] 蒲凌杰, 曾繁慧, 汪培庄. 因素空间理论下基点分类算法研究[J]. 智能系统学报, 2020, 15(3): 528–536
PU Lingjie, ZENG Fanhui, WANG Peizhuang. Base point classification algorithm based on factor space theory[J]. CAAI transactions on intelligent systems, 2020, 15(3): 528–536
[14] 刘海涛, 郝传波, 傅贵. 因素空间下的煤与瓦斯突出预测方法[J]. 黑龙江科技大学学报, 2017, 27(4): 354–358
LIU Haitao, HAO Chuanbo, FU Gui. Study on factor space- based prediction method of coal and gas outburst[J]. Journal of Heilongjiang University of Science & Technology, 2017, 27(4): 354–358
[15] REICHENBACH H, REICHENBACH M. The direction of time[M]. Berkeley: University of California Press, 1956.
[16] HáJEK A, HITCHCOCK C. The Oxford handbook of probability and philosophy[M]. New York: Oxford University Press, 2016.
[17] SPRENGER J. Foundations of a probabilistic theory of causal strength[J]. The philosophical review, 2018, 127(3): 371–398.
[18] FAGHIHI U, BOUCHARD S M, BISKRI I. Science of data: a new ladder for causation[M]//Sayed-Mouchaweh M. Explainable AI Within the Digital Transformation and Cyber Physical Systems. Cham: Springer, 2021: 33–45.
[19] 黄影平. 贝叶斯网络发展及其应用综述[J]. 北京理工大学学报, 2013, 33(12): 1211–1219
HUANG Yingping. Survey on Bayesian network development and application[J]. Transactions of Beijing Institute of Technology, 2013, 33(12): 1211–1219
[20] 曲国华, 曾繁慧, 刘增良, 等. 因素空间中的背景分布与模糊背景关系[J]. 模糊系统与数学, 2017, 31(6): 66–73
QU Guohua, ZENG Fanhui, LIU Zengliang, et al. Background distribution and fuzzy background relation in factor spaces[J]. Fuzzy systems and mathematics, 2017, 31(6): 66–73
[21] 张艳妮, 曾繁慧, 郭嗣琮. 因素空间理论的因素Markov过程[J]. 辽宁工程技术大学学报(自然科学版), 2019, 38(4): 385–389
ZHANG Yanni, ZENG Fanhui, GUO Sicong. Factor Markov process of factor space theory[J]. Journal of Liaoning Technical University (natural science edition), 2019, 38(4): 385–389
- 相似文献/References:
-
[1]熊柳,曹谢东,李杰,等.SCADA安全因素神经元的云推理机研究与仿真[J].智能系统学报,2016,11(5):688.[doi:10.11992/tis.201509020]
XIONG Liu,CAO Xiedong,LI Jie,et al.Study and simulation of the SCADA security factors neuron’scloud inference engine[J].CAAI Transactions on Intelligent Systems,2016,11():688.[doi:10.11992/tis.201509020]
[2]曲国华,李春华,张强.因素空间中属性约简的区分函数[J].智能系统学报,2017,12(6):889.[doi:10.11992/tis.201609014]
QU Guohua,LI Chunhua,ZHANG Qiang.Attribute reduction and discernibility function in factor space[J].CAAI Transactions on Intelligent Systems,2017,12():889.[doi:10.11992/tis.201609014]
[3]刘劲威,曹谢东,徐诗艺,等.云模型油气SCADA系统信息安全评价研究[J].智能系统学报,2018,13(2):296.[doi:10.11992/tis.201609001]
LIU Jinwei,CAO Xiedong,XU Shiyi,et al.Research on information security evaluation of oil and gas SCADA system in cloud model[J].CAAI Transactions on Intelligent Systems,2018,13():296.[doi:10.11992/tis.201609001]
[4]包研科,汪培庄,郭嗣琮.因素空间的结构与对偶回旋定理[J].智能系统学报,2018,13(4):656.[doi:10.11992/tis.201706057]
BAO Yanke,WANG Peizhuang,GUO Sicong.Structure of factor space and the dual convolution theorem[J].CAAI Transactions on Intelligent Systems,2018,13():656.[doi:10.11992/tis.201706057]
[5]汪培庄,周红军,何华灿,等.因素表示的信息空间与广义概率逻辑[J].智能系统学报,2019,14(5):843.[doi:10.11992/tis.201810021]
WANG Peizhuang,ZHOU Hongjun,HE Huacan,et al.Factorial information space and generalized probability logic[J].CAAI Transactions on Intelligent Systems,2019,14():843.[doi:10.11992/tis.201810021]
[6]崔铁军,汪培庄.空间故障树与因素空间融合的智能可靠性分析方法[J].智能系统学报,2019,14(5):853.[doi:10.11992/tis.201807022]
CUI Tiejun,WANG Peizhuang.Intelligent reliability analysis method based on space fault tree and factor space[J].CAAI Transactions on Intelligent Systems,2019,14():853.[doi:10.11992/tis.201807022]
[7]崔铁军,李莎莎.安全科学中的故障信息转换定律[J].智能系统学报,2020,15(2):360.[doi:10.11992/tis.201811004]
CUI Tiejun,LI Shasha.Conversion law of fault information in safety science[J].CAAI Transactions on Intelligent Systems,2020,15():360.[doi:10.11992/tis.201811004]
[8]蒲凌杰,曾繁慧,汪培庄.因素空间理论下基点分类算法研究[J].智能系统学报,2020,15(3):528.[doi:10.11992/tis.201903031]
PU Lingjie,ZENG Fanhui,WANG Peizhuang.Base point classification algorithm based on factor space theory[J].CAAI Transactions on Intelligent Systems,2020,15():528.[doi:10.11992/tis.201903031]
[9]崔铁军,李莎莎.系统故障因果关系分析的智能驱动方式研究[J].智能系统学报,2021,16(1):92.[doi:10.11992/tis.202003001]
CUI Tiejun,LI Shasha.Intelligent analysis of system fault data and fault causal relationship[J].CAAI Transactions on Intelligent Systems,2021,16():92.[doi:10.11992/tis.202003001]
[10]崔铁军,李莎莎.基于因素空间的人工智能样本选择策略[J].智能系统学报,2021,16(2):346.[doi:10.11992/tis.202003002]
CUI Tiejun,LI Shasha.Sample selection strategy of artificial intelligence based on factor space[J].CAAI Transactions on Intelligent Systems,2021,16():346.[doi:10.11992/tis.202003002]
备注/Memo
收稿日期:2022-06-04。
基金项目:辽宁省教育厅资助项目（JYTQN2023210，LJKZZ20220047）；阜新市社会科学课题（2023Fsllx154，2023Fsllx017）.
作者简介:曾繁慧，教授，主要研究方向为基于因素空间的数据挖掘理论与应用。入选“辽宁省兴辽英才计划”，主持、参与完成中国工程院重点项目、国家自然科学基金项目、辽宁省科研基金项目等10余项。获首批国家一流课程、辽宁省研究生教学成果一等奖等80余项奖励。发表学术论文70余篇。E-mail：597873883@qq.com。;胡光闪，硕士研究生，主要研究方向为因素空间理论下的数据挖掘和智能决策。E-mail：1599546002@qq.com;汪培庄，教授，博士生导师，主要研究方向为模糊数学及其在人工智能中的应用，是我国模糊数学的传播者和主要学术带头人，曾任国际模糊系统协会副主席，于1982年在国际上创立因素空间的数学理论，现被公认为人工智能的数学基础理论。现为辽宁工程技术大学的特聘教授，并任智能工程与数学研究院院长，荣获中华人民共和国70周年纪念章。发表学术论文200余篇，出版学术著作10余部。E-mail：peizhuangw@126.com。
通讯作者:曾繁慧. E-mail: 597873883@qq.com
更新日期/Last Update:
1900-01-01