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- In order to discover probabilistic decision rules in preferential multiple attribute decision system with incomplete information, an extension of the rough sets model is proposed in the paper. 摘要为了从有偏好信息但信息不完全的多属性决策系统中获取概率决策规则，提出一种新的不完全信息的多属性粗糙决策分析方法。
- probabilistic decision rules 概率决策法则
- There are no decision rules with which to choose decision rules. 它可以告诉我们该选用哪一条规则来进行操作。
- An approach of reduction decision table is proposed to induce decision rules. 并在此基础上，给出了一种决策表的简化方法，推导出决策规则。
- At last, the authors get the decision table and distill decision rules. 最后得到决策表的简化表，并从中提取出决策规则。
- Finally, a new prototype system with 596 decision rules in it, based on fuzzy inference, is developed. 最后，本文发展出一个以模糊推论为基础且含596条决策规则的新原型系统。
- Decision inference of the extended decision rules was implemented using fuzzy CRI (compositional rule of inference). 用关系合成的模糊推理方法，实现了扩展决策规则集的决策推理。
- With the basic principle of rough set, customers dig out the decision rules they wanted. 利用粗糙集理论的基本原理,挖掘出用户需要的决策规则。
- Third, we propose a new way of decision rules extracting based on the discriminated matrics. 再次，提出了一种基于可分辨矩阵的决策规则提取新方法。
- A method for calculating decision rules core based on binary discernibility matrix is presented directly. 基于二进制可辨矩阵给出一个简单的直接求取决策规则核的方法,并提出一种决策规则的约简算法。
- The experiment results on data sets in Rosetta software and comparison show that the algorithm provides more precise and simple decision rules. 实验结果表明，采用所提出的算法获得的规则更为简洁和高效。
- Meanwhile,decision rule and quantificational standard for proper blank element size have been brought forward. 提出了合适的坯料网格尺寸的确定原则及其定量标准。
- Concerning decision table, the concept of partial granulation was introduced, and an algorithm of dynamic mining of decision rules based on partial granulation was proposed. 摘要针对决策表，引入了偏序粒的概念，提出了一种基于偏序粒的动态决策规则提取算法。
- Most Chinese text classification methods are applied to the machine learning technologies,while ignoring the traditional methods based on decision rules. 随着基于机器学习的文本自动分类方法成为主流分类技术,基于机器学习的文本分类方法往往忽视了对规则分类方法的有效运用。
- An Application of Probabilistic Decision Tree in Bioinformatics Database 概率决策树在生物信息数据库中的一个应用
- Both the certainty and uncertainty basis decision rules were extracted directly by using rough sets theory,and then formed libraries of decision rules of the information system. 利用粗糙集理论直接生成确定性基本决策规则和不确定性基本决策规则,形成信息系统的基本决策规则库。
- Both the certainty and uncertainty basis decision rules were extracted directly by using rough sets theory, and then formed libraries of decision rules of the information system. 利用粗糙集理论直接生成确定性基本决策规则和不确定性基本决策规则，形成信息系统的基本决策规则库。
- Level priority relation was used to replace indiscernibility relation,the decision rules and multi-criteria ranking problems were studied based on priority rough sets. 以级优关系代替不可分辨关系,研究了基于优势粗糙集的决策规则获取和多准则排序问题。
