一种基于遗传算法的Rough集多知识抽取方法

Rough集理论为知识约简提供了一种有效的方法．提出了一种基于遗传算法的Rough集多知识抽取方法，针对决策系统中知识约简的不唯一性，构造了一种多约简算法，创建了多知识．在此基础上，利用遗传算法从一个更高的层次对多知识进行优化，并从中抽取最优知识集，试验结果分析表明，通过遗传算法优化后抽取的多知识较单体知识具有更高的精度，使知识的表示更具广义性。     

基于Rough集的物流决策支持系统的应用研究

在物流管理信息系统的基础上,文中基于Rough理论,采用改进的决策规则的最大覆盖方法,建立了一种决策支持模型。验证了该决策系统优于决策树和模糊逻辑方法,具有较高的决策支持度。     

一种基于Rough集的属性值约简算法

文章将Rough集理论应用于不同类型的决策表(一致决策表和不一致决策表)的约简,给出了广义决策、决策规则的一致程度、属性值重要性等定义,在此基础上提出了一种基于Rough集的属性值约简算法。该算法不仅能得到更为简洁的决策规则,而且能保持决策规则的一致程度不变。实例分析表明该算法是可行的。     

基于Rough集的连续属性离散化方法

连续属性离散化是Rough集理论应用中面临的主要问题之一.提出了一种基于的Rough集连续属性离散化方法.首先提出主泛化决策等概念,在数据过滤方法的基础上,利用等价类的合并对属性离散化.实验表明,利用该方法对数据进行离散预处理后提取的规则具有较好的分类预测准确性.     

基于缺省规则的决策支持方法*

利用Rough集理论的基本原理和方法,在提出一种缺省规则挖掘策略和算法的基础上,系统地描述了基于缺省规则的决策支持方法,将其应用于汽车故障诊断决策分析中.试用表明,该方法能较好地排除噪声的影响,使决策者在有限的时间和有限的知识下,作出比较合理的决策.     

一种扩展的不完备决策系统的Rough集方法

提出了一种基于多决策属性和决策属性可能取不确定值即"*"值的、扩展的不完备决策系统的Rough集方法,定义了系统中对象间关于条件属性的相容关系和关于决策属性的偏序关系,并以此建立了相应的近似集和决策规则为真及最优的基本概念,得出了这类系统的约简方法.     

Rough集理论及其在地质图形系统中的应用

20世纪80年代初,波兰数学家Z.Pawlak提出Rough Set(RS)的数学理论,直到10年之后,由于这个理论在机器学习、从数据库中发现知识、决策支持与分析等方面的应用,才引起人工智能(AI)研究者的关注.介绍了Rough理论基本概念和方法,并将Rough集理论引入地质图形系统领域,给出了Rough集理论用于地质图形系统中属性分析和知识发现的方法.为地质图形系统的属性信息处理开辟了一条新途径.     

基于DTRS模型的邮件过滤方法研究

邮件过滤是当前网络信息安全研究的一个热点。针对传统邮件过滤方法容错能力方面的不足,提出一种基于决策粗糙集模型DTRS(Decision-Theoretic Rough Set)的邮件过滤方法。通过将无法明确判断的邮件用DTRS的边界域进行刻画,实现正常邮件、垃圾邮件和可疑邮件的三枝决策,确保总体决策的完备性。仿真实验结果表明文中方法是有效的,并且在控制邮件误分类上具有优势。     

基于Rough集的规则学习研究

Rough Sets方法是一种处理不确定或模糊知识的重要工具,本文在对Rough Sets理论进行深入研究的基础上,提出了一种基于Rough Sets的自增量学习算法,该算法利用简化的差异矩阵和置信度,能较好地进行确定性规则和非确定性规则的学习。     

基于Rough集的噪音抑制系统的设计与仿真

采用传统方法对非固定噪音抑制系统做出决策是较为复杂的。而采用Rough集理论进行智能推理，构造独特的决策系统能较好地实现噪音抑制。仿真结果表明Rough集理论应用于噪音抑制系统所取得的效果是符合电信信道需求的。     

A Comparison of the Rough Sets and Recursive Partitioning Induction Approaches: An Application to Commercial Loans

Credit scoring is the term used to describe methods utilized for classifying applicants for credit into classes of risk. This paper evaluates two induction approaches, rough sets and decision trees, as techniques for classifying credit (business) applicants. Inductive learning methods, like rough sets and decision trees, have a better knowledge representational structure than neural networks or statistical procedures because they can be used to derive production rules. If decision trees have already been used for credit granting, the rough sets approach is rarely utilized in this domain. In this paper, we use production rules obtained on a sample of 1102 business loans in order to compare the classification abilities of the two techniques. We show that decision trees obtain better results with 87.5% of good classifications with a pruned tree, against 76.7% for rough sets. However, decision trees make more type–II errors than rough sets, but fewer type–I errors.     

A survey of decision making methods based on certain hybrid soft set models

Fuzzy set theory, rough set theory and soft set theory are all generic mathematical tools for dealing with uncertainties. There has been some progress concerning practical applications of these theories, especially, the use of these theories in decision making problems. In the present article, we review some decision making methods based on (fuzzy) soft sets, rough soft sets and soft rough sets. In particular, we provide several novel algorithms in decision making problems by combining these kinds of hybrid models. It may be served as a foundation for developing more complicated soft set models in decision making.     

集对优势关系下多粒度决策粗糙集的可变三支决策模型

多粒度决策粗糙集是从多角度来处理不确定数据和风险决策问题的重要模型。针对不完备信息系统下的决策分析问题,在多粒度决策粗糙集中引入集对优势关系,对优势度进行了改进,使结果更加合理。然后对多粒度近似空间进行了拓展,提出了集对优势关系下的乐观、悲观、均值、乐观-悲观和悲观-乐观5种多粒度决策粗糙集模型,并讨论了其相关性质以及模型之间的相互关系。结合三支决策理论,在不完备信息系统中用区间值表示损失函数,获得不同的阈值,建立了5个相应的可变三支决策模型,推导出决策规则。最后,通过公司员工评估的案例证明,所提模型在实际应用中灵活性更高,不会过于宽松或过于严格,使最终决策更为合理,从而为不完备信息系统下不确定性问题的决策分析提供了新方法。     

数据挖掘中基于Rough Set方法研究

随着数据挖掘的兴起,有许多分类和预测的方法。数据挖掘研究的实旌对象多为关系型数据库,这给粗糙集方法的应用带来了极大的方便。关系表可被看作为粗糙集理论中的决策表,而利用粗糙集理论来处理数据挖掘有着传统挖掘工具所不具有的优点。粗糙集理论是一种处理不确定和不精确问题的数学工具,文中通过实例介绍了粗糙集的基本理论,并通过实例详细介绍了在基于对决策表属性约简的基础上采用了可变精度粗糙模型实现规则的获取。该实例说明了对于不完备的信息系统,应用粗糙集理论进行数据挖掘是非常有效的。     

一种基于粗糙集理论的最简决策规则挖掘算法

研究粗糙集理论中可辨识矩阵，扩展了类别特征矩阵，提出一种基于粗糙集理论的最筒决策规则算法．该算法根据决策属性将原始决策表分成若干个等价子决策表．借助核属性和属性频率函数对各类别特征矩阵挖掘出最简决策规则．与可辨识矩阵相比，采用类别特征矩阵可有效减少存储空间和时间复杂度。增强规则的泛化能力．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．     

基于粒度决策熵的属性约简

近年来,人们越来越关注粗糙集中的属性约简算法,尤其是启发式的约简算法。为了度量属性重要度,人们把各种不同的信息熵模型应用到粗糙集中,同时在信息熵这一理论的基础上得出了许多约简算法,用来解决粗糙集中属性约简的问题。然而,现有的基于信息熵的方法还存在一系列问题。针对这些问题,本文首先将知识粒度与相对决策熵这2个概念结合在一起,从而引入一种新的信息熵模型--粒度决策熵;然后,利用粒度决策熵来度量属性的重要性,并由此得出新的约简算法--ARGDE约简算法;最后,用不同的UCI数据集来做实验,通过与已有的约简算法比较,该算法能够得到更好的实验结果。     

Multi-level rough set reduction for decision rule mining

Most previous studies on rough sets focused on attribute reduction and decision rule mining on a single concept level. Data with attribute value taxonomies (AVTs) are, however, commonly seen in real-world applications. In this paper, we extend Pawlak’s rough set model, and propose a novel multi-level rough set model (MLRS) based on AVTs and a full-subtree generalization scheme. Paralleling with Pawlak’s rough set model, some conclusions related to the MLRS are given. Meanwhile, a novel concept of cut reduction based on MLRS is presented. A cut reduction can induce the most abstract multi-level decision table with the same classification ability on the raw decision table, and no other multi-level decision table exists that is more abstract. Furthermore, the relationships between attribute reduction in Pawlak’s rough set model and cut reduction in MLRS are discussed. We also prove that the problem of cut reduction generation is NP-hard, and develop a heuristic algorithm named CRTDR for computing the cut reduction. Finally, an approach named RMTDR for mining multi-level decision rule is provided. It can mine decision rules from different concept levels. Example analysis and comparative experiments show that the proposed methods are efficient and effective in handling the problems where data is associated with AVTs.     

基于粗糙集约简的决策林构建方法

针对如何提高决策林的分类精度问题,提出一种基于粗糙集约简构建决策林的技术,包括基于逐次数据约筒构建粗糙决策林和基于遗传算法构建粗糙决策林。对3个UCI数据集的验证表明,基于遗传算法构建的粗糙决策林获得了更好的分类效果。     

粗糙集分类算法中的近似决策规则和规则匹配方法

粗糙集分类算法在应用标准决策规则进行新对象分类时,经常碰到决策规则与新对象不完全匹配的情况。因此,近似决策规则和部分匹配方法常用于提高决策规则与新对象匹配的可能性。本文在概述和比较两种近似决策规则生成算法的基础上,以一个文本分类系统为例,提出了一种综合的、更有效的近似决策规则生成算法。文章还介绍了几种通用的规则匹配方法,提出了一系列实用的完全匹配和部分匹配公式。实验表明,新提出的近似决策规则生成算法和规则匹配公式能够有效地提高决策规则与新对象的匹配可能性与准确性。     

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

The degree of malignancy in brain glioma is assessed based on magnetic resonance imaging (MRI) findings and clinical data before operation. These data contain irrelevant features, while uncertainties and missing values also exist. Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information. In this paper, a rough set method is applied to predict the degree of malignancy. As feature selection can improve the classification accuracy effectively, rough set feature selection algorithms are employed to select features. The selected feature subsets are used to generate decision rules for the classification task. A rough set attribute reduction algorithm that employs a search method based on particle swarm optimization (PSO) is proposed in this paper and compared with other rough set reduction algorithms. Experimental results show that reducts found by the proposed algorithm are more efficient and can generate decision rules with better classification performance. The rough set rule-based method can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Networks (FRE-FMMNN). Moreover, the decision rules induced by rough set rule induction algorithm can reveal regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy, which are helpful for medical experts.