(7) 信息增益方法(Information Gain)信息增益方法是机器学习的常用方法，在过滤问题中用于度量已知一个特征是否出现于某主题相关文本中对于该主题预测有多少信息。
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针对上述问题，提出了信息增益(IG)与主成分分析(PCA)相结合的特征选择方法。
Aiming at the preceding problem, this paper puts forward a feature selection method using Information Gain (IG) and Principle Component (Analysis) (PCA).
针对文本分类中信息增益降维方法的不足，提出了一种基于相对文档频的平衡信息增益(RDFBIG)降维方法。
To overcome the shortage of information gain in text categorization, this paper proposes a method of feature reduction based on the relative document frequency balance information gain (RDFBIG).
这四种特征选择采用的统计方法是：卡方、信息增益、互信息、交叉熵。
The four kinds of feature selection statistics include Chi-square, information gain, and mutual information and cross entropy, and the four corresponding feature sets are obtained.
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