Classification is an important technique in data mining. The decision trees built by most of the existing classification algorithms commonly feature over-branching, which will lead to poor efficiency in the subsequent classification period. In this paper, we present a new value-oriented classification method, which aims at building accurately proper-sized decision trees while reducing over-branching as much as possible, based on the concepts of frequentpatt-node and exceptive-child-node. The experiments show that while using relevant analysis as pre-processing, our classification method, without loss of accuracy, can eliminate the over-branching greatly in decision trees more effectively and efficiently than other algorithms do.