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。
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.
将改进的P SO引入到决策树建树方法中，并与传统的决策树方法及使用遗传算法改进后的树进行比较，验证了其优越性。
Building up decision tree by improved PSO, the paper gives the example to validate that the improved algorithm is better than the original decision tree method and by improved by GA.
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。
The algorithm constructs decision tree using an improved ID3 algorithm, and fills the missing data by decision rules.
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。
To make more scientific decision, an improved decision tree algorithm weighted ID3 is proposed and applied into the determination of aluminum tapping volume.
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。
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.
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.
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。
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.
文章针对传统的决策树生成算法之不足，提出了两种改进算法。
Two improved deci-sion tree generation algorithms are proposed. The efficiency of improved algorithms are verified.
针对传统的决策树生成算法之不足，提出了两种改进算法。
Two improved algorithms for decision tree generation are proposed. The efficiency of the improved algorithms are verified.
介绍了决策树算法的含义和构筑方法，对基于加权平均粗糙度构造决策树算法进行改进，通过实例说明了改进算法的优势。
In the process of constructing a decision tree, weighted mean roughness, a new concept based on rough set theory which is regarded as the criteria for choosing attributes is applied.
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。
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。
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.
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