The result shows that the information gain decreases before strong earthquake.
结果表明，强震前有信息增益减小的特点。
This paper proposes a new future selection algorithm based on information gain and chi-square test.
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。
An optimizing algorithm based on information gain is put forward for multisensor detection and classification of multitargets.
基于信息增量提出了一种多传感器对多目标检测与分类的优化算法。
To the problem of network level sensor management, a centralized network multi-platform sensor management algorithm based on joint information gain is proposed.
针对网络级传感器管理问题，提出一种基于联合信息增量的集中式网络多平台传感器管理算法。
By utilizing probability statistic model and maximization of information gain, this paper presents a method of sensor resource distribution for dynamic target detection.
利用概率统计模型和信息增量最大化对运动目标检测进行传感器资源调度。
This paper adopts a filtering tactic which USES candidate mapping tactic and information gain tactic to reduce the amount of concepts and attribute waiting for computing.
该文采用过滤策略，利用候选映射策略和信息增益策略减少待映射概念和待计算属性数量。
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.
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。
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。
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).
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。
Information gain is one of the most effective feature selections, but its computation is too huge.
信息增益是最有效的特征选择算法之一，但计算量大。
Aiming at the preceding problem, this paper puts forward a feature selection method using Information Gain (IG) and Principle Component (Analysis) (PCA).
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。
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。
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。
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