To solve the problem of complex computing and classifying of patterns in fault diagnosis, a fractal fault-tolerant diagnosis method based on rough reduction is presented.
针对故障诊断中计算量大，模式分类复杂的问题，提出了一种基于粗糙集的分形容错故障诊断方法。
Reduction and core are two important concepts in rough set theory, while computing reductions and core according to the definitions directly is a typical NP problem.
约简与核是粗糙集理论的两个重要概念，而直接由定义来计算约简与核是一个典型的NP难题。
Proposed an algorithm of attribute reduction based on attribute importance of rough set.
提出一种基于粗糙集属性重要性的属性约简算法。
The attribute reduction is one of the cores of Rough Set (RS) theory.
属性约简是粗糙集(RS)理论的核心内容之一。
The Rough Set Theory can handle such problems as data reduction, data mining, the evaluation of attribute importance, the formation of decision algorithm etc.
利用粗集理论处理的主要问题包括：数据简化、数据相关性的发现、数据意义的评估、由数据产生决策算法等。
The system mainly consist of four modules: the factor collect reduction based on rough set, multiple linear regression, BP net training and BP net test.
本文还设计了一个标准收入测算系统，该系统主要包括利用粗集理论对因素集进行约简、多元回归分析、BP网络训练和BP网络测试四个测算模块。
The prime concept of software testing is presented, then introduces the prime concept of rough set theory and the data reduction method based on rough set.
文中首先阐述了软件测试相关的基本概念，然后介绍了粗糙集理论基本概念和基于粗糙集理论的数据约简。
Finally, rough set theory is applied to discretization and attribution reduction of decision information table, in order to make the decision rule of image segmentation algorithm selection.
最后应用粗糙集理论来对决策信息表进行离散化处理和属性约简，以生成图像分割算法选取的决策规则。
In this paper, a rough sets based global reduction approach, which is suitable for imaging spectrometer image is proposed.
提出一种对高光谱遥感影像波段集合进行整体缩减的方法。
A reduction algorithm based on rough set is improved and then applicated to extract the rules of text categorization.
改进了一种粗糙集决策表的值约简算法，并将其应用到文本分类规则的提取中。
The technology of attribute reduction based on the intuitionistic fuzzy rough set theory is described as to the problem of information loss in the process of discretization.
本文针对传统的离散化技术所造成的信息丢失问题，提出了利用直觉模糊粗糙集合理论来进行属性约简的方法。
Simulating the hierarchical principle of human cognizance process, a hierarchical reduction algorithm of rough set theory is proposed in this paper.
本文模拟人类认知的分层递阶原则，提出一种粗糙集理论的分层递阶约简算法。
This paper mainly discusses topics on solving attribute reduction problems by applying rough set methods in the field of scientific data mining.
本论文主要讲述数据挖掘中采用粗糙集方法实现数据预处理中冗余属性约简的问题。
Finding the core (s) and the reduction of information system is prime of rough sets by the method of indiscernibility relation from origin information system.
从原始信息系统出发，用不可分辨类的思想求解系统的核属性和约简是粗集理论的精华。
The rough sets reduction model is established by integrating rough sets theory with ID3 algorithm based on statistics, uncertainty fuzzy data set information can be processed with the model.
把粗糙集理论与基于概率统计ID3算法结合建立粗糙集约简模型，可处理不精确和模糊数据集信息。
Four kinds of condition entropy are defined in this paper. Accordingly, four kinds of entropy based methods for the attribute reduction in the rough set data analysis are proposed.
本文定义了四种条件熵，并在此基础上提出了四种基于熵的方法，以用于粗糙集数据分析中的属性简约。
Knowledge reduction is the core of Rough set. It obtains the reduction attribute and classification rules while holding the ability of classification unchanged.
知识约简是其中的核心内容，是在保持分类能力基本不变的情况下，获得系统的约简属性和分类规则。
Rough sets theory provides a framework in which indiscernibility relations and Boolean reasoning form a foundation for data reduction and decision rule generation.
粗糙集理论的概念性框架之一就是利用不可分辨关系和布尔推理作为数据约简和获取决策规则的基础。
In this paper, the attribute reduction of rough set is discussed from the viewpoint of granular computing, the concepts of granularity is defined and a new attribute reduction algorithm is presented.
该文从粒度计算的角度对粗糙集理论的属性约简进行研究，定义了粒度的概念，并在此基础上提出了一种新的属性约简算法。
This paper discussed the attribute reduction in rough set combined fuzzy relation theory, and then proposed a new attribute reduction algorithm and gave an illustrative example.
结合模糊关系的理论，对粗糙集理论的属性约简算法进行研究，提出了一个新的属性约简算法，并给出了一个应用实例。
In the rough set theory and rough-fuzzy set theory, computation of approximations and edge and attributes reduction of decision table is import part of them.
在粗糙集理论及粗糙模糊集理论中，上下近似及边界的求解与决策表属性约简是它们的核心内容。
In this paper, we study the problem of rules reduction for real value information systems by using of rough set theory.
本文研究应用粗集理论对实值信息系统属性进行约简的方法。
On the basis of giving a new type of attribute reduction method, a coupling recognition model is established which combines Rough Sets and neural network closely.
在提出一种属性约简方法的基础上，利用粗糙集和径向基网络的优势，构建了一种耦合模型。
Based on the attribute reduction of rough set, from different aspects, this paper sets up indicator screening model based on rough set.
基于粗糙集的属性约简原理，本文从不同的角度建立了基于粗糙集的指标筛选模型。
With regard to the attribute values in decision table, which are described with hybrid data, a new algorithm of attribute reduction based on rough set theory is proposed.
针对决策表中属性取值为杂合数据的情况，提出了基于粗糙集理论的属性约简算法。
The method combined CHI value feature selection and rough set theory fully so as to avoid both feature reduction on a large scale decision table and the discretization of the decision table.
该方法将CHI值特征选取和粗糙集理论充分结合，避免了用粗糙集对大规模决策表进行特征约简，同时避免了决策表的离散化。
From this view a partition of the boundary of a rough set is obtained and the concepts of band rough set and band distribution reduction and maximum distribution reduction are presented.
基于这一思想提出了分级粗糙集模型和分级最大分布约简、分级分布约简的概念。
Using rough set of the final value reduction algorithm for text classification rules extraction, thus gained the final text classification rules.
然后采用粗糙集的值约简算法来进行文本分类规则的抽取，从而得到最终的文本分类规则。
Using rough set of the final value reduction algorithm for text classification rules extraction, thus gained the final text classification rules.
然后采用粗糙集的值约简算法来进行文本分类规则的抽取，从而得到最终的文本分类规则。
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