2) multiple decision trees
多重决策树
1.
For classification problems in data mining, based on thought of combination classification method, this paper proposes a combination classification method of multiple decision trees based on genetic algorithm.
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。
2.
<Abstrcat> For customer classification problems in customer relationship management (CRM), this paper proposes a combination classification method of multiple decision trees based on genetic algorithm.
针对客户关系管理中的客户分类问题,提出一种基于遗传算法的多重决策树组合分类方法。
3.
Based on thought of multiple classifiers combination method,this paper proposes a combination classification method of multiple decision trees based on PSO Algorithm.
针对数据挖掘中的分类问题,根据多分类器融合的思想,提出一种基于粒子群优化算法的多重决策树分类器融合方法。
3) multi-classifier class-combiner
多基决策树联合决策
4) multivariate decision tree
多变量决策树
1.
Application of rough set to build transformer fault diagnosis model based on multivariate decision tree;
粗糙集用于建立基于多变量决策树的变压器故障诊断模型
2.
Principal Component Analysis-based Approach for Multivariate Decision Tree Construction;
基于主成分分析的多变量决策树构造方法
3.
A knowledge roughness based approach to multivariate decision tree construction;
基于知识粗糙度的多变量决策树的构建
5) multivariable decision tree
多变量决策树
1.
Research on mining rules from incomplete information systems based on multivariable decision tree;
基于多变量决策树的不完备信息系统规则提取研究
2.
The paper presents a multivariable Decision Tree Algorithm on the use of attribute reduction and generalization based on rough set theory,for a easier and higher accuracy tree,especially using the indicator that weighted average roughness to select testing attributes and to build decision tree.
为了使构造的决策树更简单,规则更容易被理解且精度更高,文章基于粗糙集理论提出了一种对属性约简及泛化的多变量决策树算法。
6) Multi-Relational Decision Tree
多关系决策树
1.
Improved multi-relational decision tree algorithm
改进的多关系决策树算法
2.
In view of the above problems, this paper do the following work:First of all, this paper has studied on data mining theory, multi-relational data mining theory , Especially it has studied deeply on multi-relational classification algorithms,multi-relational decision tree and latest technology of multi-relational data mining -tuple ID propogation.
针对以上问题,本文主要做了以下工作:首先,本文对数据挖掘理论、关系数据挖掘理论进行了研究,尤其是多关系数据挖掘的分类算法-多关系决策树算法及多关系数据挖掘的最新技术-元组传播技术进行了深入的研究。
补充资料:决策树
分子式:
CAS号:
性质: 一种可用于处理多阶段决策问题的决策图。由于这种图形似树枝,故称为决策树。它由决策点,方案技,概率点(又称状态点),概率枝(又称状态枝)顺序延伸而成,最右端是益损值见图(图暂缺)。决策时,从右至左,先算出各个概率点的益损期望值,并分别标注在各概率点上。然后对各概率点(即方案)的益损期望加以比较,即选出最大的益损期望值并标注在决策点的上方、与最大期望值相应的即为最优方案,然后决定解的去留,直到最后找到选好解。
CAS号:
性质: 一种可用于处理多阶段决策问题的决策图。由于这种图形似树枝,故称为决策树。它由决策点,方案技,概率点(又称状态点),概率枝(又称状态枝)顺序延伸而成,最右端是益损值见图(图暂缺)。决策时,从右至左,先算出各个概率点的益损期望值,并分别标注在各概率点上。然后对各概率点(即方案)的益损期望加以比较,即选出最大的益损期望值并标注在决策点的上方、与最大期望值相应的即为最优方案,然后决定解的去留,直到最后找到选好解。
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
参考词条