In the end, decision tree classification experiments results and contrastive precision accuracy are obtained.
最后进行了分步决策树分类实验和与传统分类方法的精度对比分析。
There are some various algorithms in data mining, and decision tree classification algorithm is the most popular one.
在数据挖掘中存在多种算法，决策树分类算法是应用比较多的一种。
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。
This paper introduce the classification algorithm of using decision tree, and to compare and evaluate for the various decision tree classification algorithm.
介绍了利用判定树分类的算法，并对各种判定树分类算法进行比较和评价，在此基础上提出判定树分类算法的改进方向。
Then the decision tree and class association rules mining are used on the video attribute database to extract a decision tree classification rule set and a class association rule set respectively.
然后分别使用决策树、分类关联规则等技术对视频属性数据库进行数据挖掘，提取出决策树分类规则集和分类关联规则集；
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 creating a classification tree (a decision tree), the data can be mined to determine the likelihood of this person to buy a new M5.
创建一个分类树(一个决策树)，并借此挖掘数据就可以确定这个人购买一辆新的M5的可能性有多大。
The second was classification (also known as classification tree or decision tree), which can be used to create an actual branching tree to predict the output value of an unknown data point.
第二种技术是分类（即分类树或决策树），用来创建一个实际的分支树来预测某个未知数据点的输出值。
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。
In the process of constructing a decision tree, the criteria of selecting partitional attributes will influence the efficiency of classification.
在构造决策树的过程中，分离属性选择的标准直接影响分类的效果。
Decision tree is one of the models that are often used in classification, and it has been widely researched and applied since it was proposed in 1966.
决策树是分类中常用的模型之一，自1966年被提出以来已经得到了广泛的研究和应用。
Compared with artificial classification, nerve network, and decision tree, its test error is low and the speed is high.
该方法与人工分类、神经网络、决策树等方法比较，其测试误差低，测试速度高。
This paper proposes a text classification method based on Cloud Theory and neural network structure decision tree.
提出一种基于云理论和神经网络构造决策树的文本分类方法。
When analyze financial factor, decision tree method on the basis of inductive reasoning means is adopted to analyze the infection of financial factor to loan risk classification.
在财务因素分析上，采用基于归纳推理方法的决策树方法，分析了财务因素对贷款风险分类的影响。
This paper introduces the classification model of random decision tree and how to heuristic selected the depth and the number, the experiment shows that the algorithm is effectiveness and efficiency.
该文介绍了随机决策树分类模型及如何启发式选择随机决策树的深度及棵树，通过实验证明了该算法的有效性和高效性。
The introduction of generalized decision tree(GDT) realized the unification of classification rules and decision tree structure.
文章引入了广义决策树的概念，实现了分类规则集和决策树结构的统一。
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。
In this paper, a new method which combines unsupervised and supervised learning strategy is put forward to construct the multi classification decision tree.
提出了一种融合无监督和监督两种学习策略生成多分类决策树的方法。
A hierarchical decomposed support vector machines binary decision tree is used for classification.
采用一种层次分解的支持向量机二叉决策树进行分类识别。
Decision tree learning strategy have long been popular in pattern recognition, machine learning, and other disciplines for solving problems concerned with the classification.
决策树学习策略广泛应用于模式识别和机器学习等领域，用来解决与分类相关的问题。
Moreover, this thesis compares FCA with clustering analysis, and analyzes the similarities and differences of classification based on concept lattice and decision tree.
另外，本文在相关章节对形式概念分析和聚类分析进行比较以及分析总结了基于概念格的分类和决策树分类法的异同。
Decision tree, as a flow chart, is structure of a tree, which is mostly used in finding classification rules and prediction of classification.
决策树是一个类似于流程图的树结构，主要用途是提取分类规则，进行分类预测。
Methods: the decision tree classifier is used as a tool and the rate of classification accuracy is used to measure the consistency.
方法：以决策树分类器为工具，用分类正确率衡量辨证一致性。
The results of experiment demonstrate that the SVM decision tree built up by this method has a good classification performance.
实验结果表明，这种方法构造的SVM决策树分类器分类性能较好。
There are many classification methods to forecast such as decision tree algorithm (C4.5), Bayes algorithm, BP algorithm and SVM.
现有的分类预测的方法有许多种，常见的有决策树算法(C4.5)、贝叶斯分类算法、BP算法与支持向量机等。
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。
Using teacher images and machine learning method, an image direction classification model is built as a decision tree. Test results argued the validity of this method.
实验结果表明，系统所使用的轮廓线向量图像特征也能够较有效地应用于图像方向分类，而机器学习则能够有效地为之建立决策树分类模型。
Decision tree is an important method in induction learning as well as in data mining, which can be used to form classification and predictive model.
决策树是归纳学习和数据挖掘的重要方法，通常用来形成分类器和预测模型。
The decision tree is a frequently-used classification method in data mining, it is easy to understand and large in scope to apply.
决策树是数据挖掘技术中一种常用的分类方法，易于理解，应用范围广泛。
The decision tree is a frequently-used classification method in data mining, it is easy to understand and large in scope to apply.
决策树是数据挖掘技术中一种常用的分类方法，易于理解，应用范围广泛。
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