基于粗集理论的属性权重确定方法

通常属性权重的确定方法是根据决策者的先验知识来确定。本文根据粗集理论中属性重要度的判断方法,提出将决策者先验知识给定的权重同粗集理论确定的属性重要度结合起来最终确定属性权重,即基于粗集理论的属性权重确定,实现主观先验知识同客观情况的统一,从而得出更加理想的权重确定结果。     

一种基于粗集的实例分解归纳学习方法

粗集是知识约简的一种有效工具,但决策表的最小约简已被证明是NP难题.对于复杂的决策表,由于必要的条件属性数目可能较多,直接用粗分析比较困难.为此本文基于粗集理论,给出了一种决策表的分解方法.实例表明决策表的分解可以减少知识约简的难度.这对于大型的决策表,有潜在的应用价值.     

基于神经网络的软件质量评估

粗集与神经网络的集成反映了人类正常的思维机制.它融合了定性和定量的,精确和非确定的,连续和平行的方法.本文建立了粗集(RS)的BP神经网络并进行属性约简的混合模型,对软件质量特性的关键值和软件系统质量予以评估,并提出了有效的算法.     

粗分析在智能决策支持中的应用

专家知识的归纳获取是智能系统知识自动获取的有效方法.本文针对目前归纳学习中存在的问题,指出粗集理论用于决策支持弥补了传统决策方法的不足.此外还分析粗集和其它软计算方法的集成问题,综述了基于粗集理论的软计算在智能决策支持中的应用,并提出了粗集发展的几个可能方向.     

基于粗集的IT项目风险因素多元组合权重研究与聚类分析

属性权重研究是IT项目风险分析的重要过程,在分析属性权重、研究同类方法的基础上,指出了通过传统方法以及粗集方法确定单个属性权重存在的一些问题,由此提出了基于粗集理论的属性组合权重的概念与计算方法,最后根据组合权重的相似系数矩阵,对风险因素进行聚类分析,为项目管理提供决策依据.     

基于粗糙集和神经网络的供应商决策选择研究

本文提出了一种基于粗集理论与神经网络相结合的供应商优化选择新方法.首先利用粗集理论对供应商原始指标决策表进行一致性属性约简,然后借助神经网络强大的学习能力对决策表进行自学习,完成属性的不一致约简,最后再由粗糙集对其进行值约简,得到最终的供应商优化选择决策规则,并给出了相应的算法.最后通过实例说明该方法的有效可行性.     

一种基于Rough集的客户流失风险分析方法

为了对客户流失风险分析过程中的大量的冗余特征进行约简或压缩,本文利用粗集理论中的特征约简方法来研究客户流失风险分析,提出了一种基于粗集的客户流失风险的分析方法.通过一个客户流失风险的分析实例对该算法进行了检验,实验结果表明,在保证分类质量基本不变的情况下,该算法可以查找出对分类起主要作用的特征,从而达到了特征约简的目的,成功地将粗集理论应用到客户流失风险的分析和预警中,为客户流失风险的分析和预警提供了一条新的研究思路和方法.     

基于粗集理论的一种归纳学习算法

归纳学习是粗集理论的主要应用领域.本文基于粗糙集理论,针对相容决策表,提出一种新的归纳学习算法.与以往的方法相比,这种算法比较简单,能够全面地获取规则且没有冗余.此外,还讨论了不相容决策表的学习,给出了具有较高可信度的规则挖掘过程.     

基于邻接网络的频繁项目集发现算法

频繁项目集发现一直都是关联规则研究领域中最繁项目集发现算法,该算法的特别之处在于事先利用有向图进行的一次数据预处理,在预处理过程中将数据库预先存贮为每个结点都有一个域来记录其支持度的项目集邻接网络,从而把复杂的频繁项目集的发现问题转化为简单的图中搜索问题,这就大大提高了频繁项目集发现过程的效率.同时为了有效地解决预处理过程中的项目集支持度计算问题,采用了一种纵向的数据库表示格式.最后对所采用的算法给出实验结果.     

粗集与神经网络相结合的股票价格预测模型

粗集和神经网络结合反映了人类智能的定性和定量、清晰和隐含、串行和并行相互交叉混合的常规思维机理。本文建立这样一种混合杂交模型用于股票价格波动趋势的预测,通过粗集对数据的二维约简预处理消除了样本中的噪声和冗余,在提高神经网络预测精度的同时降低了学习负担。为了获得最优的预测精度,本文还利用遗传算法进行属性离散化和网络学习。通过对上证综指的实证研究表明,这种混合杂交模型的性能明显优于BP和GA神经网络模型。     

成套产品定价方法研究

在给出成套产品定价的概念的基础上,通过一个简单的算例来说明成套产品定价方法的思想,给出了纯成套产品定价方法的总收益大于产品单卖时的总收益的条件,并给出了混合成套产品定价时最优成套出售的套数k^#存在的区间。     

A Note on Balancedness of Dominating Set Games

In this paper we consider the balancedness of dominating set games, introduced by Velzen 2003. We establish a new kind of 0-1 program formulation to model the domination problem on graphs, and give a strong connection between LP relaxation of this 0-1 program and the cost allocation problem concerning the core of a dominating set game. Duality theory on Lagrange dual is the main technique in our proof. In particular, we use this insight to give the equivalence of the balancedness for two different dominating set games. Supported by NSFC (No. 10371114) and KRF-2003-002-C00038     

Improved Approximation for Breakpoint Graph Decomposition and Sorting by Reversals

Sorting by Reversals (SBR) is one of the most widely studied models of genome rearrangements in computational molecular biology. At present, is the best known approximation ratio achievable in polynomial time for SBR. A very closely related problem, called Breakpoint Graph Decomposition (BGD), calls for a largest collection of edge disjoint cycles in a suitably-defined graph. It has been shown that for almost all instances SBR is equivalent to BGD, in the sense that any solution of the latter corresponds to a solution of the former having the same value. In this paper, we show how to improve the approximation ratio achievable in polynomial time for BGD, from the previously known to for any > 0. Combined with the results in (Caprara, Journal of Combinatorial Optimization, vol. 3, pp. 149–182, 1999b), this yields the same approximation guarantee for n! – O((n – 5)!) out of the n! instances of SBR on permutations with n elements. Our result uses the best known approximation algorithms for Stable Set on graphs with maximum degree 4 as well as for Set Packing where the maximum size of a set is 6. Any improvement in the ratio achieved by these approximation algorithms will yield an automatic improvement of our result.     

Variable precision rough set theory and data discretisation: an application to corporate failure prediction

Since the seminal work of Pawlak (International Journal of Information and Computer Science, 11 (1982) 341–356) rough set theory (RST) has evolved into a rule-based decision-making technique. To date, however, relatively little empirical research has been conducted on the efficacy of the rough set approach in the context of business and finance applications. This paper extends previous research by employing a development of RST, namely the variable precision rough sets (VPRS) model, in an experiment to predict between failed and non-failed UK companies. It also utilizes the FUSINTER discretisation method which neglates the influence of an ‘expert’ opinion. The results of the VPRS analysis are compared to those generated by the classical logit and multivariate discriminant analysis, together with more closely related non-parametric decision tree methods. It is concluded that VPRS is a promising addition to existing methods in that it is a practical tool, which generates explicit probabilistic rules from a given information system, with the rules offering the decision maker informative insights into classification problems.     

基于方法集化的动态组合评价方法研究

为了解决多种动态评价方法评价结论非一致性问题,提出动态组合评价方法。给出关于可比动态评价方法集和相容动态评价方法集的定义,并运用模糊聚类分析方法确定相容动态评价方法集;然后以相容动态评价方法集为基础确定可能的组合评价集,建立评价方法集化模型,寻找一个最满意的组合评价,并对该评价结论进行相容性检验。最后用一个实例说明本研究实际应用上的有效性。基于方法集化的动态组合评价方法对解决多种动态评价方法评价结论非一致性问题提供了思路,是综合评价方法研究的有益补充,可以利用该方法进行应用研究。     

Inapproximability and approximability of maximal tree routing and coloring

Let G be a undirected connected graph. Given g groups each being a subset of V(G) and a number of colors, we consider how to find a subgroup of subsets such that there exists a tree interconnecting all vertices in each subset and all trees can be colored properly with given colors (no two trees sharing a common edge receive the same color); the objective is to maximize the number of subsets in the subgroup. This problem arises from the application of multicast communication in all optical networks. In this paper, we first obtain an explicit lower bound on the approximability of this problem and prove Ω(g^1−ε)-inapproximability even when G is a mesh. We then propose a simple greedy algorithm that achieves performance ratio O√|E(G)|, which matches the theoretical bounds. Supported in part by the NSF of China under Grant No. 70221001 and 60373012.     

Efficient Semiparametric Estimation via Moment Restrictions

Conditional moment restrictions can be combined through GMM estimation to construct more efficient semiparametric estimators. This paper is about attainable efficiency for such estimators. We define and use a moment tangent set, the directions of departure from the truth allowed by the moments, to characterize when the semiparametric efficiency bound can be attained. The efficiency condition is that the moment tangent set equals the model tangent set. We apply these results to transformed, censored, and truncated regression models, e.g., finding that the conditional moment restrictions from Powell's (1986) censored regression quantile estimators can be combined to approximate efficiency when the disturbance is independent of regressors.     

A fuzzy logic evaluation system for commercial loan analysis

This paper introduces an application of fuzzy logic to commercial loan analysis. It describes the fuzzy sets and linguistic variables that contribute to the financial evaluation. It also presents a computer-based Fuzzy Logic Evaluation System consisting of the components Specify Fuzzy Sets, Edit Rules, and Evaluator. The Evaluator performs fuzzification, evaluation, and defuzzification activities. Using company-specific and industry financial data, the current version of the system appraises a company's credit, capital, and capacity performance. A specific example for efficiency analysis illustrates the system constructs.     

Temptation and Self‐Control

We study a two‐period model where ex ante inferior choice may tempt the decision‐maker in the second period. Individuals have preferences over sets of alternatives that represent second period choices. Our axioms yield a representation that identifies the individual's commitment ranking, temptation ranking, and cost of self‐control. An agent has a preference for commitment if she strictly prefers a subset of alternatives to the set itself. An agent has self‐control if she resists temptation and chooses an option with higher ex ante utility. We introduce comparative measures of preference for commitment and self‐control and relate them to our representations.     

A Heuristic for the Maximum Independent Set Problem Based on Optimization of a Quadratic Over a Sphere

For a given graph the maximum independent set problem is to find a maximum subset of vertices no two of which are adjacent. We propose a heuristic for the maximum independent set problem which utilizes classical results for the problem of optimization of a quadratic function over a sphere. The efficiency of the approach is confirmed by results of numerical experiments on DIMACS benchmarks.