1) attribute classification rough degree
属性分类粗糙度
1.
The algorithm takes a novel measure-attribute classification rough degree as the heuristic of choosing attribute at a tree node, which more synthetically measures contribution of an attribute for classification than other measures in Rough set and is simpler in calculation than information gain an.
采用一个新的选择属性的测度——属性分类粗糙度作为选择属性的启发式,该测度较Rough中刻画属性相关性的测度正区域等更为全面地刻画了属性分类综合贡献能力,并且比信息增益和信息增益率的计算更为简单采取了一种新的剪枝方法——预剪枝,即在选择属性计算前基于变精度正区域修正属性对数据的初始划分模式, 以更有效地消除噪音数据对选择属性和生成叶节点的影响。
2.
For the problem that the measures for measuring attribute classification ability in Rough set can only reflect the size of the object set discriminated by attributes but the synthetic contribution ability of attributes for classification,a new synthetic measures —attribute classification rough degree(ACRD) is proposed for measuring attribute classification contribution ability in Rough set.
针对Rough集中刻画属性分类能力的测度正区域等仅能反映属性可辨识对象集大小,不能反映属性对样本的划分状况影响分类的其它因素的问题,提出了Rough集中度量属性分类贡献能力的综合测度———属性分类粗糙度,对其特性进行了分析,给出了用该测度以及信息增益等分别作为决策树算法选择属性的启发式对UCI几个数据集的挖掘结果。
2) Sets Classify Roughness
集合分类粗糙度
1.
This paper introduces a new way for selecting attributes,called Sets Classify Roughness,which is based on Rough sets theory and the characteristic of the decision tree and can elevate the level of leaf node to a degree when the total number does not change.
针对构造决策树时,分类属性的选择直接影响分类效果的问题,提出了一种新的属性选择分类标准——集合分类粗糙度,该标准结合了ROUGH集知识表示与决策树构造的特性,能在总数不变的情况下,使叶结点的层次在一定程度上上浮。
3) Rough classification
粗糙分类
4) rough membership
粗糙隶属度
1.
Firstly,the original monitoring data are discretized to a decision making table,then the attributions are reduced from the decision making table so as to eliminate the factors having no or less effect on the effect-quantity,and,lastly,the proportion of affecting factors on the effect-quantity is calculated with rough membership.
首先对原始监测信息进行数据离散化得到决策表,然后对决策表进行属性约简以去除影响极小的影响因素,最后用粗糙隶属度分析各主要因素的重要性指标及其在效应量中所占的分量比例。
2.
Then, the main factors importance value and their percentage in effect quantity were computed based on rough membership.
首先对原始监测信息进行粗糙集处理,提取主要影响因素和决策规则集,然后用粗糙隶属度分析各主要因素的重要性指标及其在效应量中所占的分量比例,并且通过对规则集的不确定性推理建立了大坝监测的粗集预报模型。
5) rough set classification
粗糙集分类
6) Granular roughness
粒度粗糙性
补充资料：Pro/Engineer Drawing 表面粗糙度
1 概述
我们可以添加标准形式的表面粗糙度，下面列出了这些标准形式
Generic 切削加工 非加工
no_value
standard value value value
7.3.2 添加表面粗糙度
选择 DETAIL> Create > Surf Finish.
如果你还没有添加任何一种表面粗糙度，或者需要添加另一种类型的表面粗糙度，你需要“Retrieve”表面粗糙度；如果你需要添加已经存在的类型，你可以使用"pick Inst"选项；也可以使用"Name"选项
选择表面粗糙度的放置方式（有引线箭头、附着实体等）
选择需要的放置实体
输入表面粗糙度的值
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