Information gain is the measurement of the attributes selection in classical decision tree algorithm-ID3, but the attributes with high information gain is not always the valuable attributes.

传统的ID3决策树算法以信息增益作为属性选择的准则值，但是信息增益大的属性并不一定就是有价值的属性。

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Compared with the classical ID3 algorithm through an example, the former can reduce the decision tree at the same time of making sure of improving classification accuracy in some certain problem.

通过实例将前向决策树算法与经典的ID 3算法进行了比较，结果表明针对某些特定的问题前者在保证分类精度不降低的同时也简化了决策树。

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Firstly, the basic knowledge about decision tree and some representative algorithms for inducing decision tree are discussed, including ID3, which is classical;

文中详细的阐述了决策树的基本知识和相关算法，并对几种典型的决策树算法进行了分析比较，如：核心经典算法—ID3算法；

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