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1.
Attribute reduction problem (ARP) in rough set theory (RST) is an NPhard one, which is difficult to be solved via traditionally
analytical methods. In this paper, we propose an improved approach to ARP based on ant colony optimization (ACO) algorithm,
named the improved ant colony optimization (IACO). In IACO, a new state transition probability formula and a new pheromone
traps updating formula are developed in view of the differences between a traveling salesman problem and ARP. The experimental
results demonstrate that IACO outperforms classical ACO as well as particle swarm optimization used for attribute reduction. 相似文献
2.
本文采用改进的传统计划行为理论模型,增加了知识-态度-行为以及利社会行为的理论基础作为变量,首次引入部门背景作为重要变量,旨在研究公务人员的节能减排行为意图的影响因素。本文开发出山西省政府部门公务人员节能减排行为意图问卷,建立了山西省政府部门公务人员节能减排行为意图模型,发现了不同部门对公务人员节能减排行为意图的影响程度和方向。结果显示模型整体契合度良好,不同部门的政府部门公务人员在知识变量、价值变量、知觉行为控制变量、行为意图变量上有显著差异。在利社会行为、主观规范变量和态度变量上没有显著差异。发改委公务人员主观规范较高,交通运输厅公务人员对未来展现节能减排行为的意愿与可能性较低。建设厅执行节能减排的后果有较低的评价。 相似文献
3.
Jhieh-Yu Shyng How-Ming Shieh Gwo-Hshiung Tzeng Shu-Huei Hsieh 《European Journal of Operational Research》2010
This study proposes a novel Forward Search and Backward Trace (FSBT) technique based on Rough Set Theory to improve data analysis and extend the scope of observations made from sample data to solve personal investment portfolio problems. Rough Set Theory mathematically classifies data into class sets. The class set with the most objects may generate one decision rule. The rules generated from RST are rough and fragmented, that are very difficult to interpret the information. An empirical case is used to generate more than 85 rules by the RST method in comparison with FSBT method which only generated 14 rules. This result can show our proposed method is better than traditional RST method based on class sets that contain the most objects. Much of human knowledge is described in natural language. It is a very important thing to convert information from computer databases into normal human language. Sample data taken from features with the same backgrounds are used to compile different portfolios that investment companies and investment advisors can employ to satisfy the investor’ needs. The method not only can provide decision-making rules, but also can offer alternative strategies for better data analysis. We believe that the FSBT technique can be fully applied in research on investment marketing. 相似文献
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5.
Rough set theory provides a powerful tool for dealing with uncertainty in data. Application of variety of rough set models to mining data stored in a single table has been widely studied. However, analysis of data stored in a relational structure using rough sets is still an extensive research area. This paper proposes compound approximation spaces and their constrained versions that are intended for handling uncertainty in relational data. The proposed spaces are expansions of tolerance approximation ones to a relational case. Compared with compound approximation spaces, the constrained version enables to derive new knowledge from relational data. The proposed approach can improve mining relational data that is uncertain, incomplete, or inconsistent. 相似文献
6.
Rough set theory has been combined with intuitionistic fuzzy sets in dealing with uncertainty decision making. This paper proposes a general decision-making framework based on the intuitionistic fuzzy rough set model over two universes. We first present the intuitionistic fuzzy rough set model over two universes with a constructive approach and discuss the basic properties of this model. We then give a new approach of decision making in uncertainty environment by using the intuitionistic fuzzy rough sets over two universes. Further, the principal steps of the decision method established in this paper are presented in detail. Finally, an example of handling medical diagnosis problem illustrates this approach. 相似文献
7.
聚类分析在公务员招聘中的应用及SPSS实现 总被引:1,自引:0,他引:1
冯梅 《数学的实践与认识》2006,36(10):46-52
用多元统计分析中聚类分析的基本原理和方法,设计公务员招聘的择优录用方案,并用SPSS实现其大量数据的计算问题,为公务员的公正招聘提供有效依据. 相似文献
8.
9.
PEI Dao-wu 《高校应用数学学报(英文版)》2014,29(3):253-264
In rough set theory, crisp and/or fuzzy binary relations play an important role in both constructive and axiomatic considerations of various generalized rough sets. This paper considers the uniqueness problem of the (fuzzy) relation in some generalized rough set model. Our results show that by using the axiomatic approach, the (fuzzy) relation determined by (fuzzy) approximation operators is unique in some (fuzzy) double-universe model. 相似文献
10.
Salvatore Greco Benedetto Matarazzo Roman Słowiński 《Annals of Operations Research》2010,176(1):41-75
We consider a problem of decision under uncertainty with outcomes distributed over time. We propose a rough set model based
on a combination of time dominance and stochastic dominance. For the sake of simplicity we consider the case of traditional
additive probability distribution over the set of states of the world, however, we show that the model is rich enough to handle
non-additive probability distributions, and even qualitative ordinal distributions. The rough set approach gives a representation
of decision maker’s time-dependent preferences under uncertainty in terms of “if…, then…” decision rules induced from rough approximations of sets of exemplary decisions. 相似文献
11.
The selection of the optimal ensembles of classifiers in multiple-classifier selection technique is un-decidable in many cases and it is potentially subjected to a trial-and-error search. This paper introduces a quantitative meta-learning approach based on neural network and rough set theory in the selection of the best predictive model. This approach depends directly on the characteristic, meta-features of the input data sets. The employed meta-features are the degree of discreteness and the distribution of the features in the input data set, the fuzziness of these features related to the target class labels and finally the correlation and covariance between the different features. The experimental work that consider these criteria are applied on twenty nine data sets using different classification techniques including support vector machine, decision tables and Bayesian believe model. The measures of these criteria and the best result classification technique are used to build a meta data set. The role of the neural network is to perform a black-box prediction of the optimal, best fitting, classification technique. The role of the rough set theory is the generation of the decision rules that controls this prediction approach. Finally, formal concept analysis is applied for the visualization of the generated rules. 相似文献
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13.
北京市SARS疫情统计分析 总被引:3,自引:0,他引:3
本文对严重急性呼吸道综合症 SA RS疾病进行了统计分析 .文章首先以北京市海淀区 SARS确诊病例和疑似病例为研究对象 ,分别按照地区、人口进行了统计分析 ,得出海淀区不同小区疫情发展的显著程度 .然后利用正交试验设计 ,分别就年龄、性别、职业等因素对海淀区 SARS确诊病人和疑似病人影响程度进行了统计分析 ,得出 2 1岁到 50岁的学生和干部得病率最高的统计结果 ,这完全符合海淀区的疫情事实 .这个统计结果已经被海淀区政府“海淀区政府应急管理信息系统 ( H EMIS)”[6]采用 . H EMIS系统作为中国第一个政府应急系统在海淀区 SARS防治中起到了一定作用 [1 ] .文章最后研究了北京市每天新增的确诊病例和每天疑似病例转为确诊病例之间的相关系数 ,从它们的相关性分析可以反映出防治 SARS疫情措施的有效性 . 相似文献
14.
《International Journal of Approximate Reasoning》2008,49(3):857-867
Rough set theory is an important tool for approximate reasoning about data. Axiomatic systems of rough sets are significant for using rough set theory in logical reasoning systems. In this paper, outer product method are used in rough set study for the first time. By this approach, we propose a unified lower approximation axiomatic system for Pawlak’s rough sets and fuzzy rough sets. As the dual of axiomatic systems for lower approximation, a unified upper approximation axiomatic characterization of rough sets and fuzzy rough sets without any restriction on the cardinality of universe is also given. These rough set axiomatic systems will help to understand the structural feature of various approximate operators. 相似文献
15.
Rough Set Theory (RST) originated as an approach to approximating a given set, but has found its main applications in the statistical domain of classification problems. It generates classification rules, and can be seen in general terms as a technique for rule induction. Expositions of RST often stress that it is robust in requiring no (explicit) assumptions of a statistical nature. The argument here, however, is that this apparent strength is also a weakness which prevents establishment of general statistical properties and comparison with other methods. A sampling theory is developed for the first time, using both the original RST model and its probabilistic extension, Variable Precision Rough Sets. This is applied in the context of examples, one of which involves Fishers Iris data.Bruce Curry: The author is grateful to two anonymous referees for various helpful suggestions 相似文献
16.
数据挖掘过程中连续属性离散化新方法研究 总被引:2,自引:0,他引:2
张文宇 《数学的实践与认识》2007,37(10):90-96
在知识发现和机器学习领域里,许多数据挖掘方法如基于粗集的数据挖掘工具等需要使用离散的属性值,但实际观测到的大多是连续性属性数据,这对许多新型数据挖掘工具的研究带来了不便.本文针对以上问题,在综合分析目前连续属性离散化方法的基础上,提出了一种基于数据分布特征的连续属性离散化新方法,并用经典算例验证了此算法,实验结果表明该方法具有合理性和可行性. 相似文献
17.
Marta Omero Lorenzo D'Ambrosio Raffaele Pesenti Walter Ukovich 《European Journal of Operational Research》2005,160(3):710-725
This paper deals with the problem of assessing the performance of a set of production units, simultaneously considering different kinds of information, yielded by a Data Envelopment Analysis, a qualitative data analysis and an expert assessment. The tool for integrating heterogeneous data is a model that applies fuzzy logic to decision support systems. The results obtained are a holistic performance assessment of each unit of the set and a ranking order of the units. 相似文献
18.
粗糙集是一种在信息系统中处理粗糙性和颗粒性的数据挖掘工具.本文从集值映射的角度研究并推广粗糙模型,使其能解决在论域和分类信息变化下对集合的近似问题.最后,讨论了集值映射下的粗糙集的性质. 相似文献
19.
《European Journal of Operational Research》2006,168(3):1019-1029
In this paper we define a new kind of mathematical programming problems. This kind, in which the decision set is a rough set, is called a rough programming problem. A rough optimal solution and a rough saddle point will be characterized. Some illustrative examples are presented. 相似文献
20.
This work promotes a novel point of view in rough set applications: rough sets rule learning for ordinal prediction is based on rough graphical representation of the rules. Our approach tackles two barriers of rule learning. Unlike in typical rule learning, we construct ordinal prediction with a mathematical approach, rough sets, rather than purely rule quality measures. This construction results in few but significant rules. Moreover, the rules are given in terms of ordinal predictions rather than as unique values. This study also focuses on advancing rough sets theory in favor of soft-computing. Both theoretical and a designed architecture are presented. The features of our proposed approach are illustrated using an experiment in survival analysis. A case study has been performed on melanoma data. The results demonstrate that this innovative system provides an improvement of rule learning both in computing performance for finding the rules and the usefulness of the derived rules. 相似文献