基于可传递信度模型的自适应k-NN分类器

针对训练模式类标签不精确的识别问题,提出了基于可传递信度模型(TBM)的自适应k-NN分类器,它通过运用pignistic变换,可以方便地对待识别模式真正所属的类做出决策,并通过梯度下降来最小化训练模式的输出类标签与目标类标签之间的误差函数,以实现参数的自适应学习.实验表明,该分类器用于处理训练模式类标签不精确的模式识别问题是有效的,且与参数优化前的基于TBM的k-NN分类器相比,其误分类率更低、鲁棒性更强.     

基于表格查寻学习算法的自适应模糊分类器

以手写体数字识别问题为背景,提出了一种基于表格查寻学习算法的自适应模糊分类 器,并用Matlab给出了自适应模糊分类器的实现,进而对其进行了仿真。仿真结果表明,该自适应模 糊分类器在手写体数字识别的识别性能、利用语言信息、计算复杂性等方面均优于采用BP算法的三 层前馈分类器,体现了自适应模糊处理技术用于模式识别的优越性和潜力。     

基于最近邻聚类算法自适应模糊分类器

以手写体数字识别问题为背景,提出了一种基于最近邻聚类算法的自适应模糊分类器,并用Matlab给出了自适应模糊分类器的实现,进而对其进行了仿真。仿真结果表明,所提出的自适应模糊分类器在手写体数字识别的识别性能、利用语言信息、计算复杂性等方面均优于采用BP算法的三层前馈分类器,体现了自适应模糊处理技术用于模式识别的优越性和潜力。     

基于自适应距离度量的最小距离分类器集成

提出了一种基于自适应距离度量的最小距离分类器集成方法,给出了个体分类器的生成方法。首先用Bootstrap技术对训练样本集进行可重复采样,生成若干个子样本集,应用生成的子样本集建立自适应距离度量模型,根据建立的模型对子样本集进行训练,生成个体分类器。在集成中,将结果用相对多数投票法集成最终的结论。采用UCI标准数据集实验,将该方法与已有方法进行了性能比较,结果表明基于自适应距离度量的最小距离分类器集成是最有效的。     

基于自适应距离度量的分类器设计方法

通过对欧氏距离度量的分析,提出了自适应距离度量.首先利用训练样本建立自适应距离度量模型,该模型保证了训练样本到相同模式类的距离最近,到不同模式类的距离最远,根据该模型建立目标函数,求解目标函数,得到最优权重.基于最小距离分类器和K近邻分类器,采用UCI标准数据库中部分数据,对提出的自适应距离度量和欧氏距离度量进行了实验比较,实验结果表明自适应距离度量更有效.     

一种具有容错能力的自适应神经网络分类器

在国内外容错型神经网络的基础上,提出了一种具有容错能力的自适应神经网络FTART4。该算法针对解决多点断路故障容错性的难点,提出了一种独特的增加网络冗余的方法,通过确定故障敏感点达到了冗余度与容错性的平衡。同时对FTART4神经网络解决多点断路故障问题进行了严谨的形式化分析,给出了FTART4算法的完整描述。采用通用的神经网络测试数据对算法进行了测试,实验结果表明该方法取得了较好的效果。     

自适应半径选择的近邻邻域分类器

在邻域粗糙集中,邻域分类器简单高效.然而,邻域半径作为决定邻域分类器分类性能的关键因素,构建方式存在不足.一方面,邻域半径的构建由于未经过训练过程而缺乏通用性;另一方面,当数据样本分布不均出现空邻域时可导致分类器失效.针对上述问题,文中提出自适应半径选择的近邻邻域分类器(Near Neighborhood Classifier with Adaptive Radius Selection, NNC-AR).首先,基于K近邻算法为训练样本构建训练邻域半径.然后,为了克服传统方法选取邻域半径参数的主观性,对待测试样本定义自适应的近邻邻域半径.最后,为分类器失效的部分测试样本定义新的近似邻域半径,有效提升分类器的泛化能力.实验表明,NNC-AR的F1值和分类精度均较高.     

基于粒子群模糊密度自适应赋值的多分类器融合算法

使用模糊积分进行融合时模糊密度的选取对于融合效果起着关键的作用,不同的模糊密度赋值就会得到不同的模糊测度,形成不同的融合函数,从而导致不同的融合识别结果。故本文提出一种用粒子群的方法对模糊密度进行自适应赋值,实验证明了谊方法的可行性。     

Human action recognition in videos based on the Transferable Belief Model

This paper focuses on human behavior recognition where the main problem is to bridge the semantic gap between the analogue observations of the real world and the symbolic world of human interpretation. For that, a fusion architecture based on the Transferable Belief Model framework is proposed and applied to action recognition of an athlete in video sequences of athletics meeting with moving camera. Relevant features are extracted from videos, based on both the camera motion analysis and the tracking of particular points on the athlete’s silhouette. Some models of interpretation are used to link the numerical features to the symbols to be recognized, which are running, jumping and falling actions. A Temporal Belief Filter is then used to improve the robustness of action recognition. The proposed approach demonstrates good performance when tested on real videos of athletics sports videos (high jumps, pole vaults, triple jumps and long jumps) acquired by a moving camera and different view angles. The proposed system is also compared to Bayesian Networks.

基于数据挖掘的网络入侵检测中k-NN分类规则改进研究

采用数据挖掘技术来扩展入侵检测的功能以判别未知攻击是当前的一个研究热点。本文在分析了各种数据挖掘算法的基础上,提出将k-NN分类规则运用于入侵检测,给出了可运用于入侵检测的k-NN分类规则改进算法k-NN for IDS。最后,我们在KDD99上对k-NN for IDS算法进行试验,验证了算法的有效性。     

Estimating parameter distributions in structural reliability assessment using the Transferable Belief Model

This paper applies the Transferable Belief Model (TBM) interpretation of the Dempster-Shafer theory of evidence to estimate parameter distributions for probabilistic structural reliability assessment based on information from previous analyses, expert opinion, or qualitative assessments (i.e., evidence). Treating model parameters as credal variables, the suggested approach constructs a set of least-committed belief functions for each parameter defined on a continuous frame of real numbers that represent beliefs induced by the evidence in the credal state, discounts them based on the relevance and reliability of the supporting evidence, and combines them to obtain belief functions that represent the aggregate state of belief in the true value of each parameter. Within the TBM framework, beliefs held in the credal state can then be transformed to a pignistic state where they are represented by pignistic probability distributions. The value of this approach lies in its ability to leverage results from previous analyses to estimate distributions for use within a probabilistic reliability and risk assessment framework. The proposed methodology is demonstrated in an example problem that estimates the physical vulnerability of a notional office building to blast loading.     

Fusing navigation and vision information with the Transferable Belief Model: Application to an intelligent speed limit assistant

The present paper focuses on the fusion, based on imprecise and uncertain information, between a Geographic Information System (GIS) and a Speed Limit Sign Recognition System (SLSRS), performed on camera images. This study is dedicated to the development of a Speed Limit Assistant (SLA) in the context of vehicle driving aid. The proposed SLA is developed within the Evidence Theory framework. The information from the sources is interpreted as belief functions using a non-antagonistic basic belief assignment (bba) in the Transferable Belief Model (TBM) semantics. This bba ensures that the conflict which could appear after the global fusion is exclusively due to source discordances. The present paper proposes a way to manage these discordances by formalizing a conflict-related constraint decision rule. As far as the application is concerned, a two-level (decentralized) fusion architecture is developed. The sensor relevancy is estimated in a first step, followed by the GIS intra-sensor fusion with a maximum of Credibility decision which determines the context-compliant speed candidate considering the road information given by the digital map. This allows the detection of possible errors of the GIS. The multi-sensor fusion then combines the GIS and SLSRS information considering that the sensors are independent and specialized in one speed, each. For the decision, two strategies are adopted. The first one considers the conflict as a threshold for the final speed selection, and so allows the SLA to stay undecided in case of highly conflicting situations. The second strategy employs the 5th version of the Proportional Conflict Redistribution operator. The SLA has been tested in simulation and in real-time experiments by qualitative and quantitative performance evaluations.     

基于TBM模型的多Agent决策融合方法

多Agent系统存在的动态特性使证据推理中的可传递置信模型（TBM）能够有效地处理动态环境的证据推理。在分析和研究可传递置信模型算法的基础上,提出一种基于证据推理TBM模型的多Agent决策融合方法,构建多Agent决策融合系统的框架模型,分析该系统的信息更新、合成算法及决策制定算法。利用SimuroSot作为仿真平台,将该方法应用于判断对手的队形和策略,得到了较满意的结果。     

短文本数据的自动分类

本文以比较购物搜索中的商品数据自动分类为应用背景,探讨短文本数据的分类问题,比较了常用的文本分类(Text Categorization)算法的特点,在此基础上提出k-NN与NB相结合的多分类器方案,对于NB算法分类不可信的情况下改用k-NN算法进行再次分类,并充分利用NB的中间结果供k-NN剪枝时作参考。实验数据表明该方法在与NB相近的时间复杂度下可明显地提高短文本分类的正确率和召回率,达到实际应用的要求。     

基于深度信念网与隐变量模型的用户偏好建模

从高维、稀疏的用户评分数据中构建用户偏好模型,存在迭代计算复杂度高、中间结果规模大和难以实现有效推理等问题。为此,提出一种基于深度信念网(DBN)和贝叶斯网(BN)的用户偏好建模方法。采用DBN对评分数据进行分类,用隐变量表示不能直接观测到的用户偏好,利用含隐变量的BN描述评分数据中蕴含的相关属性间的依赖关系及其不确定性。在MovieLens和大众点评数据集上的实验结果表明,该方法能够有效描述评分数据中与用户偏好相关的各属性间的依赖关系,其精确率和执行效率均高于隐变量模型。     

数据规范化方法对K近邻分类器的影响

讨论最小-最大规范化、z-score规范化及小数定标规范化3种方法对K近邻分类器性能的影响,在12个标准UCI真实数据集和1个人工数据集上进行实验比较。实验结果表明,规范化方法在大部分数据集能上提高K近邻分类器的识别率。针对实验结果研究据规范化方法提升分类器性能的内在原因,给出根据数据属性的数值分布特点决定是否使用数据规范化方法的一般准则。