Exploring Logical Rules Based on Causal Semantics Analysis of Relational Data

For reasoning with uncertain knowledge causal semantics analysis is investigated to propose logical rules, which can represent multi-level semantic knowledge of the relationship between the data and information implicated.These rules constitutes several tree structures named decision forest, the number of trees and stopping criteria can be set automatically. Empirical studies on a set of natural domains show that decision forest has clear advantages with respect to the generalization ability.