面向隶属度修正模糊聚类的参数选择方法

隶属度修正是模糊C-均值聚类算法改进的一个重要方向,该类改进算法引入模糊阈值修正隶属度,极大的加快了算法的收敛.然而其模糊阈值的自适应取值一直是一个较难解决的问题.针对这个问题,从数据对聚类中心的物理吸引和相似关系等角度提出了一种针对隶属度修正类FCM算法的模糊阈值参数选择方法,并从该参数选择公式的单调性、收敛性和鲁棒性等角度理论验证了该方法的有效性.仿真实验表明,该参数选择方法有效并具有较好的自适应效果,在加入离群点时也有着较强的鲁棒性,对于隶属度修正类FCM算法的参数选择有着较高的应用价值.     

一种Vague集相似度量的方案决策方法

在现有Vague集相似度量的基础上,提出一种改进的Vague集相似度量方法,并应用于工程方案的决策中。在改进的Vague集相似度量方法中考虑了Vague集的隶属度的三维含义、不确定隶属度对肯定与否定隶属程度的影响以及欧氏距离的表示。在基于Vsgue集相似度量的工程方案决策中,Vsgue集之间的相似度量是评价设计方案接近期望方案（理想方案）的度量,相似度量值越大,设计方案接近期望方案越好。这种方案决策的本质是一种模式识别方法。通过实例阐明所提出的相似度量法比现有方法有较强的分辨率,更为合理的工程方案决策,方案评价取得了满意的结果。     

基于正弦函数的直觉模糊集相似度测量及其应用

相似度测量是度量两个直觉模糊集相似程度的重要方法,现已有很多测量公式被相继提出,但现有的部分公式在一些实际应用中会出现无法归类或计算过程相对复杂现象。针对这种问题,通过对Tian 提出的余切相似度测量方法的研究,同时考虑隶属度、非隶属度和犹豫度三个方面信息,提出了两个正弦相似度测量公式,证明了公式的正确性,并用几组数值例子将其与其它测量公式进行比较分析,验证了新公式的合理性。最后通过实例说明了该方法的有效性。     

基于新的区间直觉模糊集相似性测度的模式识别

有关区间值直觉模糊数（集）的相似性研究较少,并且现有的方法在处理实际问题时效果较差。针对这个问题,提出了区间值直觉模糊数（集）相似性测度的新方法,包含了隶属度,非隶属度,犹豫度,以及后者对前两者的影响,将隶属度,非隶属度,犹豫度的相似度表示成三元组的形式,用TOPSIS的思想处理该三元组,得出一种新的有效的相似性度,证明其合理性。将其应用到模式识别实例中,验证其有效性。     

Vague集的相似度量

Vague集的相似性度量对于知识表达、模式识别等人工智能研究具有重要意义。Chen给出的关于Vague集的相似性度量体现出注重确定Vague集中总体肯定度相似的思想,但相似度量的公式存在度量上的不足,也蕴含了在Vague集的不可知部分接支持度和反对度各占一半的概率计算,这样既不客观也不合理。为此,提出了一种改进的相似度量。     

Vague集之间相似性度量的基本准则与一般方法

研究一般情形下Vague集之间的相似性度量问题。分析现有相似度度量方法的种类并指出其不足之处,提出Vague值间相似性度量的基本准则。考虑到未知信息对相似度的影响,根据基本准则提出适用于一般情况的度量公式。讨论该公式满足基本准则的必要条件,并给出具体的公式。最后通过模式识别中的实例说明其有效性。     

基于相似度模型的动物疾病确诊方法研究

为提高机器系统辅助诊断疾病的效率,分析了疾病诊断与模式识别的解决手段相似性,提出了基于可信度向量和模糊隶属度向量的疾病相似度的人工智能模式识别理论模型、数据结构模型、模式相似度构造算法与实践方法,并将其应用于计算机系统辅助诊断疾病的实践.数据显示结果表明,同自然人兽医专家个体相比,该模式能获得较高的诊断正确率,有效地降低了误诊率,具备较好的综合诊断性能.     

论模糊概念的度量

模糊度和模糊相似度是模糊集合中模糊概念度量最常用的两种方法。根据模糊度定义,提出了几种实用方便的模糊度公式,并分析了文[3]中新模糊度公式的不正确性;同时根据模糊贴近度定义,提出了一种贴近度公式。这些新公式的引入,在模式识别等实际应用中起着重要的作用。     

带参数区间值直觉模糊集及其在模式识别中的应用

首次提出了带参数区间值直觉模糊集的概念,并构造了一系列带参数的区间值直觉模糊集。接着,从已知隶属度和非隶属度出发,重点分析了单参数区间值直觉模糊集的构造。最后,构造了模式分界点的参数方程,并从理论上证明了临界值对模式识别结果有影响。模式识别实验结果表明,带参数区间值直觉模糊集方法比传统的直觉模糊集方法更具灵活性。     

非独立同分布下数值型数据的KNN算法改进

为挖掘数据的非独立同分布关系并解决传统KNN算法中存在的分类结果不准确的问题,提出一种非独立同分布下数值型数据的KNN改进算法.利用Pearson相关系数公式得出耦合相似度矩阵,通过该耦合相似度矩阵计算样本的类隶属度,通过Relief F算法思想进行特征权重的计算,根据训练样本的类隶属度和特征权重更新类别决策规则,确定待分类样本的类别.对多个UCI数据集的验证结果表明,该算法能够有效提高分类准确率.     

Uncertainty measures for rough formulae in rough logic: An axiomatic approach

Rough set theory, initiated by Pawlak, is a mathematical tool in dealing with inexact and incomplete information. Various types of uncertainty measure such as accuracy measure, roughness measure, etc, which aim to quantify the imprecision of a rough set caused by its boundary region, have been extensively studied in the existing literatures. However, a few of these uncertainty measures are explored from the viewpoint of formal rough set theory, which, however, help to develop a kind of graded reasoning model in the framework of rough logic. To solve such a problem, a framework of uncertainty measure for formulae in rough logic is presented in this paper. Unlike the existing literatures, we adopt an axiomatic approach to study the uncertainty measure in rough logic, concretely, we define the notion of rough truth degree by some axioms, such a notion is demonstrated to be adequate for measuring the extent to which any formula is roughly true. Then based on this fundamental notion, the notions of rough accuracy degree, roughness degree for any formula, and rough inclusion degree, rough similarity degree between any two formulae are also proposed. In addition, their properties are investigated in detail. These obtained results will be used to develop an approximate reasoning model in the framework of rough logic from the axiomatic viewpoint.     

A novel similarity/dissimilarity measure for intuitionistic fuzzy sets and its application in pattern recognition

Among the most interesting measures in intuitionistic fuzzy sets (IFSs) theory, the similarity measure is an essential tool to compare and determine degree of similarity between IFSs. Although there exist many similarity measures for IFSs, most of them cannot satisfy the axioms of similarity measure or provide reasonable results. In this paper, a novel knowledge-based similarity/dissimilarity measure between IFSs is proposed. Firstly, we define a new knowledge measure of information conveyed by the IFS and prove some properties of the proposed knowledge measure. Based on the proposed knowledge measure of IFSs, we construct a novel similarity/dissimilarity measure between IFSs and prove some properties of the proposed similarity measure. Then we use some illustrative examples to show that the proposed measures, though simple in concept and calculus, can overcome the drawbacks of the existing measures. Finally, we apply the proposed similarity/dissimilarity measure between IFSs in the pattern recognition problems to demonstrate that the proposed measure is the most reliable to deal with the pattern recognition problem in comparison with the existing similarity measures.     

Vague集相似度量的比较研究

给出了完整的Vague集相似度量约束准则,在此基础上对现有的Vague集相似度量的缺陷,进行了定量分析和证明。在充分考虑了支持度、反对度、未知度以及核函数概念之间相互关系后,提出了一种新的Vague集相似度量。通过对定理的证明以及与其他相似度量违反直觉例子的比较,表明新的相似度量全面考虑了各种约束准则,符合人们直观感觉。给出了新的Vague集相似度量在智能决策中的应用方法和应用实例。     

基于MMTD和兴趣偏向系数的协同过滤推荐算法

针对传统基于用户的协同过滤推荐算法存在的相似性度量不准确和缺乏对用户评分合理应用的问题,提出了一种结合中介真值程度度量（MMTD）和兴趣偏向系数的推荐算法。首先采用MMTD度量用户评分的相似性;然后利用用户评分相似性改进余弦相似性公式和Jaccard公式,得到新的基于MMTD的用户相似性度量方法;最后结合兴趣偏向系数输出推荐结果。在MovieLens-100k数据集上的实验结果表明,该方法可以在一定程度上提高用户间相似性度量的准确性,提高推荐结果的准确率和召回率。     

Discriminant analysis and similarity measure

The cosine similarity measure is often applied after discriminant analysis in pattern recognition. This paper first analyzes why the cosine similarity is preferred by establishing the connection between the cosine similarity based decision rule in the discriminant analysis framework and the Bayes decision rule for minimum error. The paper then investigates the challenges inherent of the cosine similarity and presents a new similarity that overcomes these challenges. The contributions of the paper are thus three-fold. First, the application of the cosine similarity after discriminant analysis is discovered to have its theoretical roots in the Bayes decision rule. Second, some inherent problems of the cosine similarity such as its inadequacy in addressing distance and angular measures are discussed. Finally, a new similarity measure, which overcomes the problems by integrating the absolute value of the angular measure and the l_p norm (the distance measure), is presented to enhance pattern recognition performance. The effectiveness of the proposed new similarity measure in the discriminant analysis framework is evaluated using a large scale, grand challenge problem, namely, the Face Recognition Grand Challenge (FRGC) problem. Experimental results using 36,818 FRGC images on the most challenging FRGC experiment, the FRGC Experiment 4, show that the new similarity measure improves face recognition performance upon other popular similarity measures, such as the cosine similarity measure, the normalized correlation, and the Euclidean distance measure.     

Vague模式排序法进行目标价值排序

提出从Fuzzy数据向Vague数据的两个转化公式,提出Vague集之间的相似度量公式。数据转化公式和相似度量公式是Vague模式排序法的两个基础。该方法能进行目标价值排序,也能为如何选择更重要攻击目标提供理论依据。应用实例表明Vague模式排序法是实用的。     

一种用于Web搜索的高效聚类算法

根据搜索引擎的用户查询日志库信息对用户访问模式聚类算法进行了研究,说明了用雅可比系数及加权相似性度量公式实现用户访问模式聚类的不足,提出了一种改进的Hamming距离公式,运用距离测度法实现用户访问模式聚类,给出了聚类算法。对算法的分析表明,基于偶图和改进Hamming距离公式的算法是准确和高效的。