Support vector machine ensemble using rough sets theory

A support vector machine （SVM） ensemble classifier is proposed. Performance of SVM trained in an input space eonsisting of all the information from many sources is not always good. The strategy that the original input space is partitioned into several input subspaces usually works for improving the performance. Different from conventional partition methods, the partition method used in this paper, rough sets theory based attribute reduction, allows the input subspaces partially overlapped. These input subspaces can offer complementary information about hidden data patterns. In every subspace, an SVM sub-classifier is learned. With the information fusion techniques, those SVM sub-classifiers with better performance are selected and combined to construct an SVM ensemble. The proposed method is applied to decision-making of medical diagnosis. Comparison of performance between our method and several other popular ensemble methods is done. Experimental results demonstrate that our proposed approach can make full use of the information contained in data and improve the decision-making performance.     

粗糙集理论在区域降水预报中的应用研究

用粗糙集属性约简方法选择出比较合理的预报因子组合,建立广西东南部区域日平均降水量的预报方程(预报时效为24h),并进行2006年5～6月的前汛期逐日业务预报应用试验.结果表明,采用属性约简方法建立的预报方程比传统的逐步回归预报方程有更高的预报精度,具有较好的业务应用前景.     

一种含参数的模糊互补判断矩阵元素与优先权重的逻辑关系

基于一致性转换公式,揭示一种含参数的模糊互补判断矩阵的元素与优先权重新的逻辑关系.阐述一致性及标度仅能确定方案的序关系,不能确定方案的优先权重等重要结论,进而拓展了模糊层次分析法.说明合理的选择参数α,能很大程度地满足决策者对客观事物的信息偏好,纠正人们对模糊层次分析法优先权重确定的某些错误认识.     

一种基于最大信息系数预处理的k-modes聚类方法

为解决现有k-modes聚类方法因忽略了变量属性之间的弱相关性,常造成其在实际应用中聚类性能不佳的问题,提出一种包含属性弱相关性的新k-modes聚类方法。引入最大信息系数（maximum information coefficient,MIC)度量数据集中变量属性之间的相关性;将得到的MIC值与原有距离进行融合,建立包含属性弱相关性信息的新度量方法,以增强变量属性间相关信息的完备性,建立更加精细的k-modes聚类方法;调用3种不同的数据集,将新方法与原有的k-modes聚类方法和其他改进k-modes聚类方法的性能进行对比,并通过仿真结果表明了新方法的有效性。     

主成分分析法在水资源评价中的应用

在介绍主成分分析方法的基本思想及计算方法基础上,对水资源调配评价指标进行了降维计算.结果显示筛选出的指标对原指标具有较好的代表性,简化了水资源评价问题的难度.     

基于角色的属性证书及其权限认证策略

针对大型企业内部网络中多种信息平台独立运行,用户权限管理困难的特点,提出了一种基于角色的PMI模型．并详述了其属性证书的格式、内容和基于该证书的用户权限认证策略。     

装备大修定价的模糊综合评判模型

利用模糊集分析理论,量化了影响装备大修定价的因素,建立了关于装备大修定价的模糊综合评判模型。通过实例对装备大修定价进行了定量计算并分析了结果,可为装备维修定价部门决策提供理论依据。     

函数S-粗集与F隐形图像生成-辨识

利用函数S 粗集, 给出图像生成, 提出 隐形图像的概念、构成结构、 隐形图像的属性特征和分离定理与还原定理, 给出 隐形图像在图像信息系统中的应用。     

基于灰色关联系数和D-S证据理论的区间数投资决策方法

主要研究投资方案的指标值为区间数时的投资方案选择问题.定义了效益型和成本型指标下的理想属性区间数,并且通过理想属性偏离度定义把指标值为区间数的矩阵转化为以理想属性偏离度为测度的矩阵,把不确定性问题转化为确定性问题处理.在此基础上把灰色关联分析方法与证据理论相结合,构造了一种新的信息不确定度提取方法和基本概率分配函数,探讨了不同子集间信度函数和基本概率分配函数之间的数量关系;通过对各投资方案对应于不同指标下基本概率分配函数的确定,利Dempster法则进行信息融合, 确定最佳投资方案. 算例结果表明,文中提出的方法可以得到满意的决策结果并显著降低决策的不确定性.     

Entropy-based procedures for intuitionistic fuzzy multiple attribute decision making

The class of multiple attribute decision making (MADM) problems is studied, where the attribute values are intuitionistic fuzzy numbers, and the information about attribute weights is completely unknown. A score function is first used to calculate the score of each attribute value and a score matrix is constructed, and then it is transformed into a normalized score matrix. Based on the normalized score matrix, an entropy-based procedure is proposed to derive attribute weights. Furthermore, the additive weighted averaging operator is utilized to fuse all the normalized scores into the overall scores of alternatives, by which the ranking of all the given alternatives is obtained. This paper is concluded by extending the above results to interval-valued intuitionistic fuzzy set theory, and an illustrative example is also provided.