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.
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