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This paper presents a new approach for constructing decision trees based on variable precision rough set model. The presented approach is aimed at handling uncertain information during the process of inducing decision trees and generalizes the rough set based approach to decision tree construction by allowing some extent misclassification when classifying objects. In the paper, variable precision weighted mean precision are introduced. The new algorithm effectively overcomes the influence of the noise data in structuring decision tree, reduces the complexity of decision tree and strengthens its extensive ability.