本文提出了一个基于信息增益和卡方检验的属性选择算法。
This paper proposes a new future selection algorithm based on information gain and chi-square test.
通过对原有决策树学习算法的研究，提出了以分类准确度为基础的属性选择算法；
We bring forward the newly attribute chosen algorithm based on classified certain degree of condition attributes for decision attribute.
针对原有互信息属性选择存在的偏向于低频词的问题，提出了一种改进的基于互信息的军用信息属性选择算法。
Aiming at the problem of trending to the low-frequency words existing in original mutual information method, we present an improved attribute-choosing algorithm of mi.
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