基于变精度粗糙集的决策树优化算法研究

This paper proposes a new heuristic function to build decision trees based on Variable Precision Rough Set. The Measure of Quality of Classification acts as the information function to select the condition attribute in this method. Dependency and redundancy between attributes are considered, especially the noisy data of training sets. A certain inconsistency is allowed to exist in examples of the positive regions, so the method can simplify the decision trees and improve its extensive ability.