在专家系统里使用决策树并非什么新鲜事物，但是把这种想法和大众外包（crowdsourcing）模式结合在一起实属天才之作。
Using decision trees in expert systems is nothing new, but applying that idea to a crowdsourcing model might possibly be a stroke of genius.
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.
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。
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.
系统采用决策树方法作为数据挖掘方法的基本算法，采用训练与学习相结合实现土地定级估价。
For land grading and evaluating, training and learning method is adopted and the integration of them implemented.
以决策树作为学习机制构建以规则为中心的专家系统，具有易理解性、灵活性，构造速度快等特点。
The rule based expert system, which regarded the decision tree as the learning mechanism has the characteristic of comprehensible flexible and constructed rapidly.
最后，本系统还提出了利用决策树的相关理论来解决景区中生态环境安全报警问题。
Finally, this system is proposed using decision tree to resolve the relevant theories of ecological environment security alarm in the scenic spot.
提出基于二维组合属性决策树的电力系统暂态稳定评估方法。
A method for the power system transient stability assessment based on 2d combined attribute decision tree was proposed.
决策树是用来解决风险型决策问题时使用的一种分析工具，具体是用树形图来分析和选择行动方案的一种系统分析方法。
Tree of decision is a kind of analyzing tool used to solve the problems of risk's decision. In detail, it is a systematic method using tree derivation to analyze and select action scheme.
本文采用C4.5决策树算法构建空气质量评价系统，挖掘空气污染物和空气等级关系的历史数据，建立空气污染物-空气等级智能评价模型。
Intelligent evaluation model for air quality based on C4.5 decision tree algorithm is established through the historical data of air pollutants and air quality classes in this article.
大型复杂系统的故障隔离是系统维修的重要环节，现有的故障隔离算法存在平均故障隔离时间（MFIT）长，不能快速自动生成决策树等缺点。
Long mean fault isolation time (MFIT) and being incapable of quickly automatic building of FI decision-making tree are the disadvantages of existing methods.
大型复杂系统的故障隔离是系统维修的重要环节，现有的故障隔离算法存在平均故障隔离时间（MFIT）长，不能快速自动生成决策树等缺点。
Long mean fault isolation time (MFIT) and being incapable of quickly automatic building of FI decision-making tree are the disadvantages of existing methods.
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