Reduction of the decision table: A rough approach

In this article, we generalize Pawlak's rough approach for simplifying the decision table in an information system. We consider an information system where attribute values are not always quantitative, but are rather subjective, having vague or imprecise meanings. Some objects may have attribute values that are almost identical; that is, they cannot be distinguished clearly by the attributes. This observation is analyzed here being based on fuzzy proximity relations on different domain of attributes. Finally we find out the minimal solution of the table. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1143–1150, 2004.     

Rough approximations based on intersection of indiscernibility,similarity and outranking relations

Rough sets theory has proved to be a useful mathematical tool for dealing with the vagueness and granularity in information tables. Classical definitions of lower and upper approximations were originally introduced with reference to an indiscernibility relation. However, indiscernibility relation is still restrictive for many applications. Many real-world problems deal with assignment of some objects to some preference-ordered decision classes. And, the objects are described by a finite set of qualitative attributes and quantitative attributes. In this paper, we construct the indiscernibility relation for the subset of nominal attributes, the outranking relation for the subset of ordinal attributes, and the similarity relation for the subset of quantitative attributes. Then the global binary relation is generated by the intersection of indiscernibility relation, outranking relation and similarity relation. New definitions of lower and upper approximations of the upward and downward unions of decision classes are proposed based on the global relation. We also prove that the lower and upper approximation operations satisfy the properties of rough inclusion, complementarity, identity of boundaries, and monotonicity.     

智能决策中的模糊近似

信息表通过目标集合来描述，目标通过条件属性和决策属性进行描述，在对这样的信息表分析处理过程中，粗糙集理论是一个非常有用的工具，粗糙集合理论的主要观点就是知识的上下近似，在实际中，条件属性和决策属性的概念通常是模糊的，而且可以利用模糊集合来说明，提出了基于模糊集合和粗糙集结合的一种新方法，对包含度进行了定义，给出了截近似和综合函数的概念，应用这些概念并结合具体例子讨论了条件属性和决策属性之间的关系，为决策过程中对条件属性权值的指定提供了理论基础。     

A fuzzy preference‐ranking model for a quality evaluation of hospital web sites

This article presents a quality evaluation model for measuring the performance of hospital Web sites. The model is developed on the basis of a conceptual framework, which consists of seven major e‐service quality dimensions, including tangibles, reliability, responsiveness, confidence, empathy, quality of information, and integration of communication issues of Web sites. The dimensions and their associated attributes are first obtained from published articles in the health care and information technology literature and then adapted according to the suggestions of related domain experts. Two multicriteria decision‐making methods are used in the evaluation procedure. Determined Web site evaluation dimensions and their relevant attributes are weighted using the Analytic Hierarchy Process (AHP) method. Vagueness in some stages of the evaluation required the incorporation of fuzzy numbers in the assessment process. Both fuzzy and crisp data are then synthesized using the fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) ranking method. The model is applied initially to measure the performance of the Web sites of Turkish hospitals. This study should be of interest to health care and technology practitioners and researchers, as the findings shed light on the further development of performance measurements for hospital Web sites. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1181–1197, 2006.     

Parameterized additive neat OWA operators with different orness levels

The article proposes an extension of the BADD OWA operator—ANOWA (additive neat OWA) operator—and defines its orness measure. Some properties of the weighting function associated with orness level are analyzed. Then two special classes of ANOWA operator with maximum entropy and minimum variance are proposed, and the orness of the BADD OWA operator is discussed. For a given orness level, these ANOWA operators can be uniquely determined. Their aggregation values for any aggregation elements set always monotonically increase with their orness levels. Therefore they can be used as a parameterized aggregation method with orness as its control parameter and to represent the decision maker's preference. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1045–1072, 2006.     

一种基于知识粒度的启发式属性约简算法

属性约简是粗糙集理论进行知识获取的核心问题之一。根据属性相似度与知识粒度的一致性,通过条件属性与决策属性以及条件属性之间的相似度度量,提出了一种基于知识粒度的启发式属性约简算法。根据条件属性与决策属性的相似度对条件属性进行降序排列,根据条件属性之间的相似度度量选择重要的属性,从而得到约简集合。理论分析与实验结果表明,该算法具有较高的运行效率和较好的约简效果。     

属性集变化条件下集值决策信息系统的增量属性约简方法

为了解决集值决策信息系统中的属性数量不断发生动态变化时,静态属性约简方法无法高效更新属性约简的问题,提出一种以知识粒度为启发信息的增量式属性约简方法.首先,介绍集值决策信息系统的相关概念,接着介绍知识粒度的定义并将其矩阵表示方法推广到此系统中;然后,分析增量式约简的更新机制,并基于知识粒度设计了增量式属性约简方法;最后...     

Expositing stages of VPRS analysis in an expert system: Application with bank credit ratings

The variable precision rough sets model (VPRS) along with many derivatives of rough set theory (RST) necessitates a number of stages towards the final classification of objects. These include, (i) the identification of subsets of condition attributes (β-reducts in VPRS) which have the same quality of classification as the whole set, (ii) the construction of sets of decision rules associated with the reducts and (iii) the classification of the individual objects by the decision rules. The expert system exposited here offers a decision maker (DM) the opportunity to fully view each of these stages, subsequently empowering an analyst to make choices during the analysis. Its particular innovation is the ability to visually present available β-reducts, from which the DM can make their selection, a consequence of their own reasons or expectations of the analysis undertaken. The practical analysis considered here is applied on a real world application, the credit ratings of large banks and investment companies in Europe and North America. The snapshots of the expert system presented illustrate the variation in results from the ‘asymmetric’ consequences of the choice of β-reducts considered.     

Expositing stages of VPRS analysis in an expert system: Application with bank credit ratings

The variable precision rough sets model (VPRS) along with many derivatives of rough set theory (RST) necessitates a number of stages towards the final classification of objects. These include, (i) the identification of subsets of condition attributes (β-reducts in VPRS) which have the same quality of classification as the whole set, (ii) the construction of sets of decision rules associated with the reducts and (iii) the classification of the individual objects by the decision rules. The expert system exposited here offers a decision maker (DM) the opportunity to fully view each of these stages, subsequently empowering an analyst to make choices during the analysis. Its particular innovation is the ability to visually present available β-reducts, from which the DM can make their selection, a consequence of their own reasons or expectations of the analysis undertaken. The practical analysis considered here is applied on a real world application, the credit ratings of large banks and investment companies in Europe and North America. The snapshots of the expert system presented illustrate the variation in results from the ‘asymmetric’ consequences of the choice of β-reducts considered.     

基于优势-直觉模糊粗糙模型的IT项目绩效审计评价规则获取

针对我国政府审计机关对政府投资的I`I}项目进行绩效审计评价规则知识获取的困难,考虑了条件属性取值 为优势精确值、分类结果为直觉模糊值的决策系统规则获取问题。首先比较条件属性值的大小,构建对象的优势部 域,再由对象邻域的直觉模糊值确定对象的上下近似;根据对象的上下近似和不同对象的直觉模糊值确定对象间的区 分关系,利用分辫矩阵给出知识约简和规则提取算法;最后将直觉模糊粗糙模型应用于政府I"I'项目绩效审计评价规 则的获取,得到了较为合理的IT项目绩效评价规则。     

On identifying and evaluating object architectures for real-time applications

This paper presents a methodology and tool support for the development of distributed real-time object-oriented systems, focusing on industrial automation applications. At system level, two different kinds of classes/objects are recognized: (i) application domain objects, which map directly to concepts and components of the problem domain, and (ii) design objects that are related to the functional requirements that the system have to meet. The approach provides a method for automatic identification of possible design objects architectures. A set of quality and testability metrics is proposed to evaluate the generated architectures, allowing the identification of the ‘best-fitted’ one.     

Classification and rule induction using rough set theory

Rough set theory (RST) offers an interesting and novel approach both to the generation of rules for use in expert systems and to the traditional statistical task of classification. The method is based on a novel classification metric, implemented as upper and lower approximations of a set and more generally in terms of positive, negative and boundary regions. Classification accuracy, which may be set by the decision maker, is measured in terms of conditional probabilities for equivalence classes, and the method involves a search for subsets of attributes (called 'reducts') which do not require a loss of classification quality. To illustrate the technique, RST is employed within a state level comparison of education expenditure in the USA.     

SOM‐based estimation of climatic profiles

This article introduces a self‐organizing map‐based approach for estimating the climatic profile of locations of interest situated within an area of known morphology. The potential of the proposed methodology is illustrated on a number of locations within the Greek territory, and its superiority over other—customarily used as well as novel—climatic profile estimation methodologies is demonstrated and numerically evaluated. It is envisioned that, after further development, the proposed methodology can be employed for creating accurate climatic maps of areas of known morphology. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 503–522, 2006.     

条件模式与决策类的从属关系研究

针对空缺数据项较多的不完整决策表,通过计算条件模式对决策类的隶属度、条件模式的相容类包含于决策类的程度来研究条件模式与决策类的从属关系。对这两种方法进行了分析和对比实验,表明了二者的一致性。将新元素的条件属性模式与已有条件模式进行匹配,可以得出新元素应属于的分类。     

基于决策熵的决策树规则提取方法

在决策表中,决策规则的可信度和对象覆盖度是衡量决策能力的重要指标。以知识粗糙熵为基础,提出决策熵的概念,并定义其属性重要性;然后以条件属性子集的决策熵来度量其对决策分类的重要性,自顶向下递归构造决策树;最后遍历决策树,简化所获得的决策规则。该方法的优点在于构造决策树及提取规则前不进行属性约简,计算直观,时间复杂度较低。实例分析的结果表明,该方法能获得更为简化有效的决策规则。     

基于新的条件熵的决策表约简方法

分析了在知识约简过程中现有条件熵的不足,在一致和不一致对象分开的基础上,定义了一种新的条件熵概念,以弥补现有信息熵的不足,在此基础上给出了以不等式为条件的约简判定定理;然后以条件属性子集的条件熵来度量其对决策分类的重要性,提出了一种新的知识约简启发式方法.应用实例分析的结果表明,基于新的条件熵的属性重要性是一种更准确、更有效的启发式信息,该方法时间复杂度较低,有助于搜索最小或次优知识约简.     

基于背景的加权关联规则挖掘方法

在时间越近越重要原则指导下,对记录进行时间加权,通过给出加权的公式,提出了一种基于背景的加权关联规则挖掘方法。该算法可以针对用户感兴趣的时间-地点背景进行关联规则挖掘。相比传统挖掘方法,该方法通过从低层次到高层次概念的序列映射,可以分析出不同粒度层次的相关信息,有利于决策者做出更准确的决策分析,制定更优的策略。     

An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank

Developing decision support system (DSS) can overcome the issues with personnel attributes and specifications. Personnel specifications have greatest impact on total efficiency. They can enhance total efficiency of critical personnel attributes. This study presents an intelligent integrated decision support system (DSS) for forecasting and optimization of complex personnel efficiency. DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. DEA has two roles in this study. It provides data to ANN and finally it selects the best reduct through ANN results. Reduct is described as a minimum subset of features, completely discriminating all objects in a data set. The reduct selection is achieved by RST. ANN has two roles in the integrated algorithm. ANN results are basis for selecting the best reduct and it is used for forecasting total efficiency. Finally, K-Means algorithm is used to develop the DSS. A procedure is proposed to develop the DSS with stated tools and completed rule base. The DSS could help managers to forecast and optimize efficiencies by selected attributes and grouping inferred efficiency. Also, it is an ideal tool for careful forecasting and planning. The proposed DSS is applied to an actual banking system and its superiorities and advantages are discussed.     

Set-valued information systems

Set-valued information systems are generalized models of single-valued information systems. Incomplete information systems can be viewed as disjunctively interpreted set-valued information systems. Since some objects in set-valued information systems may have more than one value for an attribute, so we define tolerance relation and use the maximal tolerance classes to classify the universe of discourse. In order to derive optimal decision rules from set-valued decision information systems, we propose the concept of relative reduct of maximal tolerance classes, and define a kind of discernibility function to compute the relative reduct by Boolean reasoning techniques. Finally, we define three kinds of relative reducts for set-valued information systems and used them to evaluate the significance of attributes.     

决策表分解及其最小属性约简研究

现有的很多属性约简算法都是由构造决策表的差别矩阵出发,将矩阵中非空元素的合取范式转化为极小析取范式。为提高对大规模数据的决策表进行约简的效率,文中指出基于U/{a}划分的最小约简算法存在的缺陷,给出以划分粒度为启发式信息,利用单个条件属性把论域划分成多个等价类,将计算整个全域上的属性约简问题转化为计算在相应划分的子区域上属性约简问题,提出了一种基于决策表分解的最小属性约简算法。理论分析和实例表明该约简算法是有效的。