Research on a Streamlined Causal Tree Algorithm Based on Factor Space Theory

Abstract

: Decision rule extraction is an important tool for artificial intelligence and data mining, but decision rule redundancy reduces the generalization ability of causal trees. In order to better reduce the size of causal trees and improve the classification accuracy, based on factor space theory and aiming at the elimination of noise and special samples in the dataset using the extension decision degree criteria, the conditional factor corresponding to the optimal extension decision degree is used as the branch node of the tree, and the abnormal state object is removed from the conditional factor, recurring to obtain the streamlined causal tree algorithm. Comparison with other classification algorithms shows that the streamlined causal tree algorithm produces the smallest causal tree size, the least redundant rules, and the best classification accuracy.