Induction learning of decision tree based on ID3 algorithm is an important branch of inductive learning now, which can be used to automatic acquisition of knowledge.
基于ID 3算法的决策树归纳学习是归纳学习的一个重要分支，可用于知识的自动获取过程。
ID3 algorithm is the most basic algorithm in the decision tree learning, and has a wide application.
ID 3算法是最基本的决策树学习算法，有很广的应用。
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。
The algorithm constructs decision tree using an improved ID3 algorithm, and fills the missing data by decision rules.
该算法应用改进的ID 3算法来构造决策树，利用决策规则对缺失值进行补充。
For a given incomplete decision table, the algorithm constructs decision tree using the improved ID3 algorithm, and fills the missing data in the process of constructing the decision tree.
对于给定的不完全决策表，该算法应用改进的ID 3算法来构造决策树，在构造决策树的过程中对遗失值进行补充。
This thesis compares and analyzes the typical decision tree algorithms, the ID3 algorithm and C4.
本文比较和分析了几种典型的决策树算法，着重对ID 3算法和C4。
Minimum entropy is chosen as a heuristic strategy in decision tree (DT) learning algorithm such as ID3.
决策树的学习算法，比如id3算法，选用最小信息熵作为启发式信息。
A decision tree by using for ID3 algorithm has been established, which evaluates the customer's credit.
并按照ID 3算法建立了决策树数据挖掘模型的例子，用于分析评估客户资信。
And it introduces some algorithms of decision tree learning such as ID3, C4.5 and feature subset selection of Inductive learning.
介绍了归纳学习中的决策树学习算法如id3、C4.5和特征子集选择问题。
To make more scientific decision, an improved decision tree algorithm weighted ID3 is proposed and applied into the determination of aluminum tapping volume.
为提高决策的科学化程度，提出了一种改进的决策树生成算法加权id3，并将其应用于铝电解生产中出铝量的设定。
Meanwhile it describes the decision tree classification algorithm in detail, analyzes the ID3, C4.5 and other prevalent decision tree algorithm.
同时详细的阐述了决策树分类算法，并对比较流行的决策树算法id3、C4.5等算法进行详细分析与比较。
In general, the optimum decision tree can be found by ID3 algorithm.
一般情况下，ID 3算法可以找出最优决策树。
About Data Mining ID3 decision tree algorithm code.
说明：关于数据挖掘中的决策树id3算法的代码。
Results The decision tree algorithm ID3 and C4.5 for medical image data mining are realized, the experiment results are given.
结果实现了ID3和C4.5算法对图像数据的分类，获得了分类的实验结果。
Results of instance comparison test verify the effectiveness of the method and conquer the most demerit of ID3 decision tree.
通过实例比较验证了该方法的有效性，克服了常规ID3算法的不足。
Firstly, some basic algorithms for inducing decision tree are discussed, including ID3, which uses information gain to select a splitting attribute when partitioning a training set;
文中详细阐述了几种极具代表性的决策树算法：包括使用信息熵原理分割样本集的ID3算法；
Data Mining Source ID3 decision tree algorithm, one of the classic algorithm.
数据挖掘中的ID 3算法源码决策树的经典算法之一。
By using the method to improve the ID3 algorithm, experiments show that the algorithm generates smaller decision tree and USES less training time than the algorithm using pre-pruning method.
利用该方法对基本ID 3决策树算法进行了改进。分析和实验表明，与先剪枝方法相比，该方法能进一步减小决策树的规模和训练时间。
Compared with the classical ID3 algorithm through an example, the former can reduce the decision tree at the same time of making sure of improving classification accuracy in some certain problem.
通过实例将前向决策树算法与经典的ID 3算法进行了比较，结果表明针对某些特定的问题前者在保证分类精度不降低的同时也简化了决策树。
Firstly, the basic knowledge about decision tree and some representative algorithms for inducing decision tree are discussed, including ID3, which is classical;
文中详细的阐述了决策树的基本知识和相关算法，并对几种典型的决策树算法进行了分析比较，如：核心经典算法—ID3算法；
Firstly, the basic knowledge about decision tree and some representative algorithms for inducing decision tree are discussed, including ID3, which is classical;
文中详细的阐述了决策树的基本知识和相关算法，并对几种典型的决策树算法进行了分析比较，如：核心经典算法—ID3算法；
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