information gain

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。

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To the problem of network level sensor management, a centralized network multi-platform sensor management algorithm based on joint information gain is proposed.

摘要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。

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Based on theory of chi-square test, interest measure and information gain, an algorithm which can be used for mining optimized association rules is presented in this paper.

文中基于chi-square检验、有趣度及信息增益理论，给出了一种挖掘优化关联规则的算法。

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Missing data imputation: Information gain based on approach

缺失值填充：基于信息增益的方法

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This dissertation puts up information gain and time priority attributes the properties of the frequency reduction strategy. 3.

本文提出了基于信息增益和时间属性优先的属性频率约简策略。（3）对规则获取策略进行了研究。

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Mining Best Attribute Set Using Information Gain

基于信息增益的最佳属性集发现方法

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The spatial-temporal characteristics of information gain before medium earthquakes

中等地震前信息增益时空变化特征

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Finally, analysis characteristic in the value of information gain via the probability of discrete degrees, filter the relatively redundant features of high frequency words, and refine the difference in information gain of selected feature application to get uniform accurate feature subsets.

最后使用离散度分析特征在每类中的信息增益值，过滤掉高频词中的相对冗余特征，并对选取的特征应用信息增益差值做进一步细化，获取均匀精确的特征子集。

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The commonly used feature selection methods include Information Gain, Mutual Information,χ 2 statistics, Expected Cross Entropy, Word Frequency method, Document Frequency method, Text Evidence and so on.

首先要进行特征选择，常用的特征选择方法包括：信息增益、互信息、χ2统计、期望交叉熵、词频方法、文档频次方法、文本证据权等。

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ID3 use category information gain ( CIG) as selection level, through converse thought, author come up with attribute information gain ( AIG) as selection level.

ID3使用类别信息增益（CIG）作为选择属性的标准，而作者在逆向思维的基础上提出了使用属性信息增益（AIG）来选择属性。

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Network Level Sensor Management Algorithm Based on Joint Information Gain

一种基于联合信息增量的网络级传感器管理算法

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The experiments proved that the Fisher criteria slightly inferior to the method of information gain, but compared with other feature extraction methods are better. 2.

实验证明，Fisher准则的方法略逊于信息增益，但比较其他特征选择方法都更好。

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