[1]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]
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A causal probabilistic inference classification algorithm based on factor space theory

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 4 Page number: 1042-1051 Column: 人工智能院长论坛 Public date: 2024-07-05
Title:
A causal probabilistic inference classification algorithm based on factor space theory
Author(s):
ZENG Fanhui1; 2;  HU Guangshan1; 2;  SUN Hui1; 2;  WANG Peizhuang1; 2
1. College of Science, Liaoning Technical University, Fuxin 123000, China;
2. Institute of Intelligence Engineering and Mathematics, Liaoning Technical University, Fuxin 123000, China
Keywords:
factor space; causal probabilistic inference taxonomy; desirability taxonomy; Bayesian network; factor probability theory; conditional probability; causality; artificial intelligence
CLC:
TP18
DOI:
10.11992/tis.202206004
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.
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