基于信息熵的属性约简在多属性决策中的应用

属性约简是在信息系统中的一个重要操作.分类是属性约简的基础,且直接在大数据集上进行属性约简往往存在效率低下的问题.以分类为基础提出了一种基于信息熵的信息系统属性约简算法.算法通过信息熵的计算,在属性约简的同时对原信息系统逐层分解,从而实现了属性的约简并缩小了搜索空间.提出了依据信息熵来确定属性的不必要性及简约属性集,应用在多属性决策中所带来的优势.     

基于优势关系的不协调区间值目标信息系统的分配约简

区间值信息系统是单值信息系统的一种推广模型,知识约简是粗糙集理论的核心问题之一,在基于优势关系下的不协调区间值目标信息系统中引入了分配约简和近似约简的概念,分别讨论了它们二者之间的关系,进一步给出了知识约简的判断定理和辨识矩阵,从而提供了在优势关系下不协调区间值目标信息系统分配约简的具体方法。     

不协调序决策信息系统的广义分配约简

在不协调序决策信息系统中,为了保证原有系统的协调性,结合优势原理,定义了形式更简单并且能更好的反映系统协调性的广义分配函数,提出了广义分配约简的概念,并给出了广义分配约简的辨识矩阵约简方法,理论分析和实例表明该方法是正确且有效率的。     

基于可辨识矩阵的模糊目标系统决策约简算法

在模糊目标信息系统决策约简和可辨识矩阵定义的基础上,讨论了可辨识矩阵的性质以及与决策约简集之间的关系.同时定义一种新的属性重要度,并将此作为启发式信息,设计了一种模糊目标决策信息系统最小决策约简算法,通过实例验证该算法简捷、有效.     

覆盖粗糙集属性约简的新算法

覆盖广义粗糙集是Pawlak粗糙集的重要推广,其属性约简是粗糙集理论中最重要的问题之一.Tsang等基于一种生成覆盖设计了覆盖信息系统属性约简算法,但并未明确指出其适用的覆盖粗糙集类型.在本文中,我们首先指出Tsang的属性约简算法适用的覆盖粗糙集是第五,第六和第七类.其次,我们通过建立覆盖与自反且传递的二元关系之间的等价关系,提出了一种时间复杂度更低的属性约简算法,并证明了本文中的属性约简方法就是Wang等所提出的一般二元关系属性约简的特例.本文不仅提出了属性约简的简化算法,还首次建立起覆盖属性约简与二元关系属性约简之间的联系,具有理论和实际的双重意义.     

决策信息系统中的拓扑学方法

在决策信息系统中引入拓扑结构,借助拓扑学的基本概念(拓扑、内部和闭包等)研究决策问题,用它们刻画决策信息系统中的一些重要概念(决策协调集、决策约简集、下近似协调集、下近似约简集、上近似协调集、上近似约简集),并利用它们把这些重要概念推广到最一般的情况,建立起相应的约简理论.     

集值信息系统在相容关系下的属性约简

借助于属性集值的相似程度在集值信息系统上定义了一种新的相客关系,给出了这种相客关系下集值信息系统的属性约简与判定,得到了集值信息系统属性约简的具体探作方法,并讨论了相似水平对集值信息系统的属性约简的影响.     

集值决策表基于邻域关系的属性约简

集值信息系统是完备信息系统的广义形式,它当中的一些对象在某些属性下的取值可能不止一个,反映的是信息的不确定性.本文在集值信息系统上引入对象的邻域关系,并以每个对象的邻域作为基本集,建立了集值信息系统的粗糙集方法.为了简化的知识表示,我们进一步讨论了邻域协调集值决策表的正域约简与邻域不协调集值决策表的近似分布约简,给出了正域约简与近似分布约简的等价刻画条件,并借助区分函数给出了计算正域约简与近似分布约简的方法.     

基于粗糙集的突发事件属性约简方法

针对突发事件不完备信息系统中的原始数据存在大量属性冗余的问题,提出一种基于粗糙集的不完备信息系统属性约简方法,以剔除冗余属性,提高知识清晰度。首先对缺失、冗余、噪声以及连续型数据进行预处理;然后进行属性分类,将属性分为条件属性与决策属性,进而建立决策表;最后根据决策表的特征,结合有序加权平均算子的思想,提出一种基于属性重要度的启发式属性约简算法。文末,通过实例验证了方法的正确性与有效性,并利用该方法实现了火灾数据的属性约简。     

协调区间值决策形式背景的知识约简

知识约简是概念格理论的核心问题之一.主要讨论协调区间值决策形式背景的知识约简问题.首先从经典的协调决策形式背景出发,定义了协调区间值决策形式背景,同时给出了协调区间值决策形式背景上决策保序集的定义和判定定理,并进一步阐明了决策保序集和协调集之间的关系,然后通过定义辨识矩阵,给出了协调区间值决策形式背景的属性约简方法.     

Dependence-space-based attribute reductions in inconsistent decision information systems

In rough set theory, attribute reduction is an important mechanism for knowledge discovery. This paper mainly deals with attribute reductions of an inconsistent decision information system based on a dependence space. Through the concept of inclusion degree, a generalized decision distribution function is first constructed. A decision distribution relation is then defined. On the basis of this decision distribution relation, a dependence space is proposed, and an equivalence congruence based on the indiscernibility attribute sets is also obtained. Applying the congruences on a dependence space, new approaches to find a distribution consistent set are formulated. The judgement theorems for judging distribution consistent sets are also established by using these congruences and the decision distribution relation.     

Updating attribute reduction in incomplete decision systems with the variation of attribute set

In rough set theory, attribute reduction is a challenging problem in the applications in which data with numbers of attributes available. Moreover, due to dynamic characteristics of data collection in decision systems, attribute reduction will change dynamically as attribute set in decision systems varies over time. How to carry out updating attribute reduction by utilizing previous information is an important task that can help to improve the efficiency of knowledge discovery. In view of that attribute reduction algorithms in incomplete decision systems with the variation of attribute set have not yet been discussed so far. This paper focuses on positive region-based attribute reduction algorithm to solve the attribute reduction problem efficiently in the incomplete decision systems with dynamically varying attribute set. We first introduce an incremental manner to calculate the new positive region and tolerance classes. Consequently, based on the calculated positive region and tolerance classes, the corresponding attribute reduction algorithms on how to compute new attribute reduct are put forward respectively when an attribute set is added into and deleted from the incomplete decision systems. Finally, numerical experiments conducted on different data sets from UCI validate the effectiveness and efficiency of the proposed algorithms in incomplete decision systems with the variation of attribute set.     

Rough sets theory for multicriteria decision analysis

The original rough set approach proved to be very useful in dealing with inconsistency problems following from information granulation. It operates on a data table composed of a set U of objects (actions) described by a set Q of attributes. Its basic notions are: indiscernibility relation on U, lower and upper approximation of either a subset or a partition of U, dependence and reduction of attributes from Q, and decision rules derived from lower approximations and boundaries of subsets identified with decision classes. The original rough set idea is failing, however, when preference-orders of attribute domains (criteria) are to be taken into account. Precisely, it cannot handle inconsistencies following from violation of the dominance principle. This inconsistency is characteristic for preferential information used in multicriteria decision analysis (MCDA) problems, like sorting, choice or ranking. In order to deal with this kind of inconsistency a number of methodological changes to the original rough sets theory is necessary. The main change is the substitution of the indiscernibility relation by a dominance relation, which permits approximation of ordered sets in multicriteria sorting. To approximate preference relations in multicriteria choice and ranking problems, another change is necessary: substitution of the data table by a pairwise comparison table, where each row corresponds to a pair of objects described by binary relations on particular criteria. In all those MCDA problems, the new rough set approach ends with a set of decision rules playing the role of a comprehensive preference model. It is more general than the classical functional or relational model and it is more understandable for the users because of its natural syntax. In order to workout a recommendation in one of the MCDA problems, we propose exploitation procedures of the set of decision rules. Finally, some other recently obtained results are given: rough approximations by means of similarity relations, rough set handling of missing data, comparison of the rough set model with Sugeno and Choquet integrals, and results on equivalence of a decision rule preference model and a conjoint measurement model which is neither additive nor transitive.     

Bayesian rough set model: A further investigation

Bayesian rough set model (BRSM), as the hybrid development between rough set theory and Bayesian reasoning, can deal with many practical problems which could not be effectively handled by original rough set model. In this paper, the equivalence between two kinds of current attribute reduction models in BRSM for binary decision problems is proved. Furthermore, binary decision problems are extended to multi-decision problems in BRSM. Some monotonic measures of approximation quality for multi-decision problems are presented, with which attribute reduction models for multi-decision problems can be suitably constructed. What is more, the discernibility matrices associated with attribute reduction for binary decision and multi-decision problems are proposed, respectively. Based on them, the approaches to knowledge reduction in BRSM can be obtained which corresponds well to the original rough set methodology.     

An incremental attribute reduction approach based on knowledge granularity under the attribute generalization

Attribute reduction is a key step to discover interesting patterns in the decision system with numbers of attributes available. In recent years, with the fast development of data processing tools, the information system may increase quickly in attributes over time. How to update attribute reducts efficiently under the attribute generalization becomes an important task in knowledge discovery related tasks since the result of attribute reduction may alter with the increase of attributes. This paper aims for investigation of incremental attribute reduction algorithm based on knowledge granularity in the decision system under the variation of attributes. Incremental mechanisms to calculate the new knowledge granularity are first introduced. Then, the corresponding incremental algorithms are presented for attribute reduction based on the calculated knowledge granularity when multiple attributes are added to the decision system. Finally, experiments performed on UCI data sets and the complexity analysis show that the proposed incremental methods are effective and efficient to update attribute reducts with the increase of attributes.     

Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough sets

Covering rough sets generalize traditional rough sets by considering coverings of the universe instead of partitions, and neighborhood-covering rough sets have been demonstrated to be a reasonable selection for attribute reduction with covering rough sets. In this paper, numerical algorithms of attribute reduction with neighborhood-covering rough sets are developed by using evidence theory. We firstly employ belief and plausibility functions to measure lower and upper approximations in neighborhood-covering rough sets, and then, the attribute reductions of covering information systems and decision systems are characterized by these respective functions. The concepts of the significance and the relative significance of coverings are also developed to design algorithms for finding reducts. Based on these discussions, connections between neighborhood-covering rough sets and evidence theory are set up to establish a basic framework of numerical characterizations of attribute reduction with these sets.     

指派问题在供应商选优决策中的应用

通常供应链中供应商选优问题为多指标决策问题,本将此问题视为指派问题。指派问题中的关键是确定“效率”矩阵,本充分利用供应商单排序结果,评价指标权重以及供应商指标评价值构造了“效率”矩阵,建立了供应商综合选优指派问题模型,案例试算表明该方法合理、有效,为多指标方案决策提供了又一种可行的决策方法。     

基于依赖空间的信息系统的属性约简

在粗糙集的信息系统中构造了依赖空间,并给出了基于依赖空间的信息系统的属性约简理论和约简方法,并举例说明其方法的有效性和可行性.