classification decision tree 分类决策树
classification decision making 分类决策
multi-classification decision tree 多分类决策树
multi classification decision tree 多分类决策树
multi-criteria classification decision-making 多准则分类决策
Decision tree classification 决策树分类法 ; 决策树分类挖掘
Firstly,generates fuzzy rules base using fuzzy clustering from numerical sample dates,and then simplifies the sample attributions using rough set theory,deletes the redundant rules,and gets the simplified fuzzy rules base,in order to make classification decision conveniently.
对于数值型样本数据,首先采用模糊聚类生成模糊规则库,然后运用粗糙集理论对样本属性进行约简,删除冗余规则,即可得到精简的模糊规则库,以方便进行分类决策。
参考来源 - 一种基于精简的模糊规则库的分类算法·2,447,543篇论文数据，部分数据来源于NoteExpress
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。
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。
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。
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