Based on the above analysis, a new algorithm of decision tree induction is proposed.
基于上述分析，提出了决策树优化算法。
With the thorough analysis on the algorithm of decision tree induction, we established a classification and prediction system based on C4.5 and accomplished the integration with the LMIS system.
通过对决策树算法的深入分析，我们围绕着C4.5决策树生成算法建立了一个分类预测系统并实现了与劳动力市场信息管理系统（LMIS）的集成。
Experiment is the traditional method for analysing multivalue BIOS of decision tree algorithm, but it has a fault that we must have the expertise of the specific field.
实验方法是分析决策树算法中的多值偏向问题的传统方法，其缺点是需要具备该数据领域的专家知识。
Decision tree algorithms are applied to the data mining of the mammography classification, proposes a medical images classifier based on decision tree algorithm, the experiment results are given.
利用决策树算法对乳腺癌图像数据进行分类，实现了一个基于决策树算法的医学图像分类器，获得了分类的实验结果。
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。
Decision tree algorithm is that the category knowledge of the training set is mined through built high precision and small-scale decision tree.
决策树算法通过构造精度高、小规模的决策树采掘训练集中的分类知识。
And on other hand, the thesis also analyzes the factors that affect the jobs of graduated student by adopting decision tree algorithm, and some valuable models are achieved.
另一方面，本文还利用决策树算法，对影响学生就业的因素进行了分析，得到了一些有价值的模式。
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 paper mainly discusses how to build a decision tree of rail deformation detection by using C4.5 algorithm and how to make decision of the rail deformation by building decision tree.
文中主要讨论如何应用C4.5算法构造列车轨道故障检测的决策树以及根据生成的决策树实现轨道故障的判决。
This essay is researching the Decision Tree Algorithm of Data Mining and the use in the Customer Drain analysis.
本文主要是研究数据挖掘中的决策树算法以及决策树算法在具体的小灵通流失分析中的研究与分析。
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。
This paper introduces two construction algorithms of Classification decision tree based on parallel algorithm, and analyzes applicability.
本文重点介绍了两种基于并行算法的分类决策树的构造算法，并对它们的适用性及特点作了分析。
Then, the paper emphasizes the theory and method of Decision Tree algorithm and realizes C4. 5 algorithm.
随后，着重分析了决策树算法的理论背景以及实现步骤，并给出了C4.5算法的伪码实现。
In order to prove the validity of the algorithm, it USES decision tree as the base learner.
为了证明算法的有效性，采用决策树作为基分类器。
According to the rice spectral features of hyperspectral image data acquired during the rice is growing, a hybrid decision tree classification algorithm dealing with the variety of rice is developed.
根据水稻生长期的高光谱数据的光谱特征，设计了一个混合决策树分类算法。
By the B-cut sets and the probabilities of the nodes of Binary Decision Diagrams (BDD), a new quantitative analysis algorithm of coherent fault tree based on BDD is presented in this paper.
利用二元决策图(BDD)中的B—割集和节点概率，提出了基于BDD的关联故障树定量分析新算法。
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。
An algorithm of the decision tree based on class feature matrix is proposed.
提出了一种基于类别特征矩阵的决策树算法。
Finally, a heuristic algorithm for rules extraction of decision tree was designed.
以新的属性重要性为启发式信息设计决策树规则提取方法。
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。
Therefore, It possesses important theoretic and practical significance to make further improvement of decision tree algorithm, make it more suitable for data mining application requirements.
因此，进一步改进决策树算法，使其更加适合数据挖掘的应用要求，具有重要的理论和现实意义。
This paper will explain the process of constructing the test node and propose a two-stage decision tree algorithm.
本文将从几何学角度说明构造测试节点的过程，提出了一种两阶段决策树的算法。
Typically, the fuzzy decision tree algorithm is an improvement of the crisp decision tree algorithm, and is an extension of the crisp decision tree algorithm.
通常，模糊决策树算法是在清晰决策树算法的基础上进行改进得到的，是对清晰决策树算法的扩展。
At last, a binary decision tree could be built. Algorithm analysis and simulation results show that RMBRDM can support rules with ranges and the performance of RMBRDM is better than that of PTS.
最后建立一棵二叉决策树。理论分析和仿真实验均表明，RMBRDM算法不仅支持以范围形式表示的规则，且时空性能优于PTS算法。
The existing majority of the decision tree inductions are based on a top-down greedy algorithm, which make a locally optimal decision at each node.
目前存在的决策树归纳算法大多数是基于自顶向下的贪婪算法，它在每个结点都执行一个局部最优决策。
And the algorithm improves the ending condition of building decision tree which don't stop constructing from using all of the attributes. So it has no influence on original data.
并且该算法改进了决策树创建叶节点的条件，从而决策树不会用尽所有的候选属性才停止构造，这就消除了没有原始数据造成的影响。
And the algorithm improves the ending condition of building decision tree which don't stop constructing from using all of the attributes. So it has no influence on original data.
并且该算法改进了决策树创建叶节点的条件，从而决策树不会用尽所有的候选属性才停止构造，这就消除了没有原始数据造成的影响。
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