首页|因素空间理论下的因果概率推理分类算法研究

因素空间理论下的因果概率推理分类算法研究

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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.
A causal probabilistic inference classification algorithm based on factor space theory
The integration of machine learning techniques with causal reasoning can significantly enhance method per-formance.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 prob-abilistic 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 demon-strate 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 ap-plication value of factor space.

factor spacecausal probabilistic inference taxonomydesirability taxonomyBayesian networkfactor prob-ability theoryconditional probabilitycausalityartificial intelligence

曾繁慧、胡光闪、孙慧、汪培庄

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辽宁工程技术大学 理学院,辽宁 阜新 123000

辽宁工程技术大学 智能工程与数学研究院,辽宁 阜新 123000

因素空间 因果概率推理分类法 可取度分类法 贝叶斯网络 因素概率论 条件概率 因果关系 人工智能

辽宁省教育厅资助项目辽宁省教育厅资助项目阜新市社会科学课题阜新市社会科学课题

JYTQN2023210LJK-ZZ202200472023Fsllx1542023Fsllx017

2024

智能系统学报
中国人工智能学会 哈尔滨工程大学

智能系统学报

CSTPCD北大核心
影响因子:0.672
ISSN:1673-4785
年,卷(期):2024.19(4)
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