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).

youdao

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).

youdao

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.

youdao

针对这一问题，提出了一种基于聚类的特征选择方法，先使用聚类的方法对特征间的冗余性进行裁减，然后使用信息增益的方法选取类别区分能力强的特征。

This paper introduced a new feature selection method, which first used clustering to reduce redundancy among features and then used Information Gain to choose good features.

youdao

针对这一问题，提出了一种基于聚类的特征选择方法，先使用聚类的方法对特征间的冗余性进行裁减，然后使用信息增益的方法选取类别区分能力强的特征。

This paper introduced a new feature selection method, which first used clustering to reduce redundancy among features and then used Information Gain to choose good features.

youdao