Combining the philosophy of clustering and information gain, a discretization algorithm based on distance and information gain is proposed.

结合聚类的思想与信息增益性质，给出一种基于距离与信息增益相结合的连续属性离散化方法。

Based on decision tree that created by utilizing information gain, the detail rules of indexes rating were given.

使用信息增益生成决策树，给出利用决策树计算指标评分细则的方法。

At the same time, the significance measurement is denoted as the information gain and an algorithm of computing reduct based on this measurement is provided.

用信息论的观点来解释属性约简的含义，提出用信息增益作为属性的重要度量，并给出了基于这种重要度量的属性约简算法。

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.

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。

The comparison experiments of speech signals corresponding to different SNR and noise type was designed using the measure of complexity behaviors based on the information gain.

采用基于信息增益的复杂性行为度量，对含不同噪声类型，以及不同信噪比的各种中英文语音样本进行了对比实验。

In order to eliminate adverse effect brought by bad classifiers and improve effect and stability of combiners , an approach extracting classifiers based on Information Gain(IG) is proposed.

为了减少表现差的个体分类器对集成器分类性能的影响，提高集成器分类效果及稳定性，提出了基于信息增益的分类器选择方法。