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把基于粗集的属性约简方法与支持向量机分类器的基本理论相结合,以提高决策分类的综合性能。将所提方法应用于医疗诊断决策,并对属性约简前后的决策性能进行了比较分析。试验结果表明,利用约简后的数据集,计算复杂性降低,内存需求减少,同时仍能保持较高的决策准确率。 相似文献
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模糊粗糙数据模型:一种数据分析的新方法 总被引:7,自引:0,他引:7
提出了一种数据分析的新方法——模糊粗糙数据模型(Fuzzy Rough Data Model,FRDM).该方法采用动态自适应模糊聚类技术,将Kowalczyk方法中的粗糙数据模型(Rough Data Model,RDM)对输入数据空间的网格状“硬划分”转化为模糊划分,辨识输入数据空间中的模糊模式类,并通过定义各模糊模式类与决策类别之间的类型映射关系ftype:Ci→y,以及输入数据对各模式类分类规则的匹配度(Degree of Fulfillment,DoF(x))概念,建立起相应的FRDM模型.不同数据集的实验测试结果表明,与Kowalczyk的RDM方法相比,文中方法具有更好的数据概括能力、更强的噪声数据处理能力和更高的搜索效率. 相似文献
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By importing the idea of P2P,and transmitting messages among clients directly into the client-server architecture,a new hybrid architecture was presented with the help of AOI technology and message category.Theoretical analysis of this architecture was presented in detail.A series of simulation experiments was carried out to verify its effectiveness.Results indicate that the new architecture produces less server message workload than traditional architectures,which can improve the scalability of DVE systems. 相似文献
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分类分析中基于信息论准则的特征选取 总被引:3,自引:1,他引:2
Feature selection aims to reduce the dimensionality of patterns for classificatory analysis by selecting the most informative instead of irrelevant and/or redundant features. In this study, two novel information-theoretic measures for feature ranking are presented: one is an improved formula to estimate the conditional mutual information between the candidate feature fi and the target class C given the subset of selected features S, i.e., I(C;fi|S), under the assumption that information of features is distributed uniformly; the other is a mutual information (MI) based constructive criterion that is able to capture both irrelevant and redundant input features under arbitrary distributions of information of features. With these two measures, two new feature selection algorithms, called the quadratic MI-based feature selection (QMIFS) approach and the MI-based constructive criterion (MICC) approach, respectively, are proposed, in which no parameters like β in Battiti's MIFS and (Kwak and Choi)'s MIFS-U methods need to be preset. Thus, the intractable problem of how to choose an appropriate value for β to do the tradeoff between the relevance to the target classes and the redundancy with the already-selected features is avoided completely. Experimental results demonstrate the good performances of QMIFS and MICC on both synthetic and benchmark data sets. 相似文献
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基于判别改进局部切空间排列特征融合的人脸识别方法 总被引:3,自引:0,他引:3
改进型局部切空间排列(ILTSA)是最近提出的一种流形学习方法。基于对ILTSA的线性逼近和判别拓展,该文提出一种新的称为判别改进局部切空间排列(DILTSA)的特征提取方法,并给出了理论证明和算法分析。基于最大邻域间隔准则和ILTSA, DILTSA能够同时保持类内与类间局部判别几何结构。此外,提出一种增强型Gabor-like复数小波变换以缓解照明和表情变化对人脸识别的影响。通过融合Gabor-like复数小波变换和原始图像特征,能够进一步提高人脸识别的准确率。在Yale 和PIE人脸数据库上的实验结果证明了所提方法的有效性。 相似文献
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基于证据理论的多类分类支持向量机集成 总被引:5,自引:0,他引:5
针对多类分类问题,研究支持向量机集成中的分类器组合架构与方法.分析已有的多类级和两类级支持向量机集成架构的不足后,提出两层的集成架构.在此基础上,研究基于证据理论的支持向量机度量层输出信息融合方法,针对一对多与一对一两种多类扩展策略,分别定义基本概率分配函数,并根据证据冲突程度采用不同的证据组合规则.在一对多策略下,采用经典的Dempster规则;在一对一策略下则提出一条新的规则,以组合冲突严重的证据.实验表明,两层架构优于多类级架构,证据理论方法能有效地利用两类支持向量机的度量层输出信息,取得了满意的结果. 相似文献