1) incremental DT
增量决策树
2) 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
3) univariate decision tree
单变量决策树
1.
Method to diagnose networks fault based on univariate decision tree;
基于单变量决策树的网络故障诊断方法
2.
It is difficult for univariate decision tree to reflect the relationship of attributes,multivariate decision tree can resolve this problem preferably,the former produces big tree,the latter gains simple tree but difficult to explain.
单变量决策树难以反映信息系统属性间的关联作用,构造的决策树往往规模较大。
4) 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。
5) hybrid decision tree
混合变量决策树
1.
Aim to upwards points,in this paper,advance a knowledge roughness based approach to hybrid decision tree,select less knowledge roughness as tested attribute to construct decision tree.
针对以上两种决策树特点,提出了基于知识粗糙度的混合变量决策树的构造方法,选择知识粗糙度较小的分类属性来构造决策树。
6) decision trees
决策树
1.
Constructional algorithm of principal and subordinate structure self-set based on decision trees;
基于决策树的主从结构的Self集构造算法
2.
Research on the performance of Decision Trees on IR;
决策树在信息检索中的性能研究
3.
Classification rules for mining tumors and normal tissues using genetic algorithms and decision trees;
基于遗传算法和决策树的肿瘤分类规则挖掘
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