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。
There are some various algorithms in data mining, and decision tree classification algorithm is the most popular one.
实验结果表明D- S决策树分类算法能有效的对不确定数据进行分类。
This D-S decision tree is a new classification method adapted to the uncertain data.
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。
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.
通过对决策树分类算法的比较，本文采用C4.5决策树算法实现自学习模块。
Comparing with Decision Tree algorithms, this system chooses the C4.5 to realize the self-learning module.
ID 3算法是数据挖掘中经典的决策树分类算法，该算法具有抗噪声能力差的缺点。
ID3 algorithm is a classical algorithm in data mining, this algorithm has the worse ability to resist noise.
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。
Meanwhile it describes the decision tree classification algorithm in detail, analyzes the ID3, C4.5 and other prevalent decision tree algorithm.
实验结果表明，D - S证据理论决策树分类算法能有效地对不确定数据进行分类，有较好的分类准确度，并能有效避免组合爆炸。
This D-S decision tree is a new classification method applied to uncertain data and shows good performance and can efficiently avoid combinatorial explosion.
分类与预测分析是数据挖掘的主要技术手段之一，至今已在理论和方法上取得了丰硕的研究成果，决策树分类算法就是其中最典型的代表。
Classify and prediction is the main measures in Data Mining, which make great progress in theory and method till now, and Decision Tree arithmetic is the symbol.
分类(也即分类树或决策树)是一种数据挖掘算法，为如何确定一个新的数据实例的输出创建逐步指导。
Classification (also known as classification trees or decision trees) is a data mining algorithm that creates a step-by-step guide for how to determine the output of a new data instance.
至今已经提出了决策树的很多算法，通过分析已知的分类信息得到一个预测模型。
So far, there are many algorithms have been given and we can gain a prediction model by analyzed known catalog information.
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。
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.
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。
Decision tree algorithm is that the category knowledge of the training set is mined through built high precision and small-scale decision tree.
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。
This paper introduces two construction algorithms of Classification decision tree based on parallel algorithm, and analyzes applicability.
大部分数据挖掘工具采用规则发现和决策树分类技术来发现数据模式和规则，其核心是归纳算法。
Most data mining tools use rule discovery and decision tree technology to extract data patterns and rules; its core is the inductive algorithm.
通过对原有决策树学习算法的研究，提出了以分类准确度为基础的属性选择算法；
We bring forward the newly attribute chosen algorithm based on classified certain degree of condition attributes for decision attribute.
基于分类决策树算法，建立了一个挖掘体系。
An intelligent mining system is created based on the decision tree algorithm.
本文提出了一种基于决策树分类器的数据包分类算法。
The thesis researches an algorithm based on decision tree classifier for packet filtering.
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。
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.
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。
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.
这种算法基于二值图像，先从二值图像中找出各数字的特征，然后再用决策树对这些数字进行分类。
The method is based on a binary image. First, the authors extract the features of every numeral in the image, then classify the numerals by a decision tree.
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。
There are many classification methods to forecast such as decision tree algorithm (C4.5), Bayes algorithm, BP algorithm and SVM.
本文从这一实际问题出发，对数据挖掘中用于分类的核心算法之一——决策树方法进行了深入地研究。
As for the problem above, the paper in-depth researches one of the core algorithms which are applied to classification-decision tree algorithm.
并给出了两种构造多维时间序列分类的决策树模型算法。
Two algorithms for structuring decision tree model of multi-dimensional time series classification were presented.
在语法层面，本文采用C4.5决策树算法将问题分类为是非问句、正反问句、特选问句和特指问句四种类型；
In syntax level, we classify the question into four categories by C4.5 algorithm, such as yes-or-no interrogative, specially related interrogative, and so on.
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。
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.
为了证明算法的有效性，采用决策树作为基分类器。
In order to prove the validity of the algorithm, it USES decision tree as the base learner.
针对数据挖掘中的分类问题，依据组合分类方法的思想，提出一种基于遗传算法的多重决策树组合分类方法。
Based on thought of multiple classifiers combination method, this paper proposes a combination classification method of multiple decision trees based on PSO Algorithm.
方法利用决策树算法对乳腺癌图像数据进行分类，提出了一个基于决策树算法的医学图像分类器。
Methods decision tree algorithms are applied to the data mining of the mammography classification, proposes a medical images classifier based on decision tree algorithm.
方法利用决策树算法对乳腺癌图像数据进行分类，提出了一个基于决策树算法的医学图像分类器。
Methods decision tree algorithms are applied to the data mining of the mammography classification, proposes a medical images classifier based on decision tree algorithm.
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