1) dependent properties reduction
依赖属性约减
1.
Before services matchmaking, according to service request, OGSDA-RS performs the standardization of service request, irrelevant properties reduction and dependent properties reduction.
针对网格服务发现的查全率、查准率效率较低的现状,基于本体技术和粗糙集理论,设计了一个服务发现算法OGSDA-RS,在服务匹配之前先进行3步预处理操作:规范化请求服务,根据请求服务对发布服务进行不相关属性约减和依赖属性约减。
2) attribute dependence
属性依赖
1.
By using the above two characteristics,the concepts of ladder knowledge,the generation of ladder knowledge and the attribute dependence of knowledge were presented,the attribute dependence mining theo-rem of knowledge and the attribute dependence mining-state recognition criterion of knowledge were proposed,and the applica-tions of attribute dependence.
给出阶梯知识,阶梯知识生成,知识属性依赖的概念,提出知识的属性依赖挖掘定理,知识的属性依赖挖掘-状态识别准则,给出知识的属性依赖挖掘的应用。
3) Default Dependence
违约依赖性
4) attribute dependency
属性依赖性
1.
A new attribute dependency and significance were defined with the independent check theory of the statistical aiming at the disadvantages of the standard for choosing the attributes of the branch nodes with the information gain in the ID3 algorithm.
针对ID3算法用信息增益作为在各级非叶节点上选择属性的标准的局限性,结合统计学独立检验思想,给出一种新的属性依赖性和重要性定义,以新的属性重要性为启发式信息设计决策树规则提取算法。
2.
The relation between absolute reducts and attribute dependency is also discussed.
此外还阐述了绝对属性约简与属性依赖性之间的关系。
6) attribute reduction
属性约减
1.
an application of neural net on attribute reduction is proposed,by which,to a greater extent,the overfitting in boost- ing is avoided.
本文提出了使用神经元网络技术进行属性约减后进行助推决策树建模的方法,较大程度上避免了助推的过度训练问题。
2.
A Algorithm for Attribute Reduction with Uncertain Factor Based on Rough Sets
粗糙集作为数据挖掘工具,主要通过分类数据得到预测型知识,但分类规则过于严格,使得挖掘结果可能会损失一些有价值的规则,本文引入带不确定性因子的决策系统UFDS,在该系统中根据统计结果和领域知识为每一对象赋以不确定度k和重要度p,并对传统等价类划分进行扩充,成为重要类和负类,在此基础上提出了带不确定因子的属性约减算法。
补充资料：减约
1.俭省节约。
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