基于自然最近邻居的社团检测算法

针对传统社团检测算法无法判断网络中特殊节点和SCAN算法对于参数依赖性太大的缺点,提出了一种基于自然最近邻居概念的社团检测算法CD3N.算法利用自然最近邻居无参的特性,首先以结构相似度为基准,计算出网络节点的自然最近邻居,并依此构造小值最近邻域图;然后取邻域图中邻居数最多的节点为核心节点,根据可达关系,构造关于核心节点的社团;重复选取核心节点并构造社团的过程,直到没有可归入社团的节点.将算法应用到空手道俱乐部网络和海豚网络中,并与SCAN算法进行对比.实验结果表明,CD3N算法有效解决了参数敏感性问题,能够很好地进行社团检测.     

基于PCA与改进的最近邻法则的异常检测

提出一种新颖的基于特征抽取的异常检测方法,先对预处理数据进行标准化变换,然后应用主成份分析(PCA)抽取入侵特征,最后应用一种改进的最近邻分类方法--基于中心的最近邻分类法(CNN)检测入侵.利用KDD Cup'99数据集,将PCA删与PCA NN、PCA SVM、标准SVM进行比较,结果显示,在不降低分类器性能的情况下,特征抽取方法能对输入数据有效降维,且在各种方法中,PCA与CNN的结合能得到最优的入侵检测性能.     

Feature selection based on loss-margin of nearest neighbor classification

The problem of selecting a subset of relevant features is classic and found in many branches of science including—examples in pattern recognition. In this paper, we propose a new feature selection criterion based on low-loss nearest neighbor classification and a novel feature selection algorithm that optimizes the margin of nearest neighbor classification through minimizing its loss function. At the same time, theoretical analysis based on energy-based model is presented, and some experiments are also conducted on several benchmark real-world data sets and facial data sets for gender classification to show that the proposed feature selection method outperforms other classic ones.     

基于近似最近邻搜索的改进PRM算法

针对移动机器人工作环境范围复杂时,使用传统概率路线图(PRM)算法非常耗时的问题,提出一种改进的PRM算法.PRM算法最耗时的部分是构建无向路径图,构建无向路径图的关键是近邻搜索.通过使用近似最近邻搜索中的局部敏感哈希算法代替原先最近邻搜索算法,在不降低生成路线图质量的前提下,加快无向路线图的构建速度,减少PRM算法的运行时间.仿真结果表明,改进的PRM算法相较于传统的PRM算法在无向路径图建立时间上减少27.36％ ～33.27％,使PRM算法效率大大提高.     

Manifold-ranking based retrieval using k-regular nearest neighbor graph

Manifold-ranking is a powerful method in semi-supervised learning, and its performance heavily depends on the quality of the constructed graph. In this paper, we propose a novel graph structure named k-regular nearest neighbor (k-RNN) graph as well as its constructing algorithm, and apply the new graph structure in the framework of manifold-ranking based retrieval. We show that the manifold-ranking algorithm based on our proposed graph structure performs better than that of the existing graph structures such as k-nearest neighbor (k-NN) graph and connected graph in image retrieval, 2D data clustering as well as 3D model retrieval. In addition, the automatic sample reweighting and graph updating algorithms are presented for the relevance feedback of our algorithm. Experiments demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms.     

预测外膜蛋白的核最近邻算法

外膜蛋白由于其位于细菌的表面,从而对于抗生素和疫苗开发具有重要的研究价值.如何准确地将外膜蛋白从球蛋白和内膜蛋白等中识别出来对于从基因组序列中确认外膜蛋白以及预测其二级、三级结构都是一项重要的研究任务.近年来人们已经提出了若干从蛋白质序列出发预测外膜蛋白的方法.本文利用1种新的核方法,即核最近邻算法,结合蛋白质序列的子序列分布预测外膜蛋白,并和支持向量机方法、传统的最近邻算法进行了比较.结果表明本文算法不亚于已有的预测方法,而且新算法更为简洁、容易实现.同时我们发现残基顺序在外膜蛋白预测中具有重要作用.     

基于最近邻域法的气体传感器模式识别技术研究

提出将气体传感器阵列检测与最近邻域法相结合的方法实现气体的模式识别。设计了用该方法进行气体识别的实验系统。该方法具有实验次数少,且识别准确度高的优点。实验以3只金属氧化物半导体气体传感器组成的阵列为例,详细讨论了该方法的实验过程与识别结果。通过对CH4,H,CO 3种气体进行识别实验,结果表明:该方法的正确识别率达到100%,具有很高的实用价值。     

基于Relief的特征加权壳近邻分类算法

壳近邻分类算法克服了k近邻分类在近邻选择上可能存在偏好的问题,使得在大数据集上的分类效果优于k近邻分类,为了进一步提高壳近邻算法的分类性能,提出了基于Relief特征加权的壳近邻分类算法.该算法在Relief算法的基础上求解训练集的特征权值,并利用特征权值来改进算法的距离度量方法和投票机制.实验结果表明,该算法在小数据和大数据上的分类性能都优于k近邻和壳近邻分类算法.     

Effective naive Bayes nearest neighbor based image classification on GPU

Non-parametric classifier, Naive Bayes nearest neighbor, is designed with no training phase, and its performance outperforms many well-trained learning-based image classifiers. Unfortunately, despite its high accuracy, it suffers from great computational pressure from distance computations in space of local feature. This paper explores accelerating strategies from perspectives of both algorithm design and software development. Our approach integrates space decomposition capability of Product quantization (PQ) and parallel accelerating capability of underlying computational platform, Graphics processing unit (GPU). PQ is exploited to compress the indexed local features and prune the search space. GPU is used to ease most of computational pressure by processing the tasks in parallel. To achieve good parallel efficiency, a new sequential classification process is first designed and decomposed into independent components with high parallelism. Effective parallelization techniques are then presented to make use of computational resources. Parallel heap array is built to accelerate the process of feature quantization. Distance table lookup is built to speed up the process of feature search. Comparative experiments on UIUC-Sport dataset demonstrate that our integrated solution outperforms other implementations significantly on Core-quad Intel Core i7 950 CPU and GPU of NVIDIA Geforce GTX460. Scalability experiment on 80 million tiny images database shows that our approach still performs well when large-scale image database is explored.     

基于近邻点数据库的室内定位技术研究

提出建立所有参考点的近邻点数据库的方法,使得原本只是待定位点与参考点之间单一的关系,拓展为待定位点与参考点和待定位点的近邻点与其他参考点之间的网状关系,充分挖掘利用了接收信号强度指示(RSSI)指纹数据库中有用的信息,有效避免了室内复杂环境的干扰.算法首先建立参考点的RSSI指纹数据库,求出每个参考点的K近邻点,建立近邻点数据库;求待定位点的K近邻点,然后从近邻点数据库中找出这K个近邻点的项中均存在的n个参考点;对得到的待定位点的K个近邻点和n个参考点的坐标加权求和,得到待定位点估计坐标.     

A new k-harmonic nearest neighbor classifier based on the multi-local means

The k-nearest neighbor (KNN) rule is a classical and yet very effective nonparametric technique in pattern classification, but its classification performance severely relies on the outliers. The local mean-based k-nearest neighbor classifier (LMKNN) was firstly introduced to achieve robustness against outliers by computing the local mean vector of k nearest neighbors for each class. However, its performances suffer from the choice of the single value of k for each class and the uniform value of k for different classes. In this paper, we propose a new KNN-based classifier, called multi-local means-based k-harmonic nearest neighbor (MLM-KHNN) rule. In our method, the k nearest neighbors in each class are first found, and then used to compute k different local mean vectors, which are employed to compute their harmonic mean distance to the query sample. Finally, MLM-KHNN proceeds in classifying the query sample to the class with the minimum harmonic mean distance. The experimental results, based on twenty real-world datasets from UCI and KEEL repository, demonstrated that the proposed MLM-KHNN classifier achieves lower classification error rate and is less sensitive to the parameter k, when compared to nine related competitive KNN-based classifiers, especially in small training sample size situations.     

基于近邻相似度图聚类的用户用电行为分析

以周负荷数据为用户用电行为分析的视角较通常使用日负荷数据更符合用户客观用电周期规律,提出一种面向用户周负荷数据聚类方法,通过改进的近邻相似度图聚类避免计算过程中维度增高导致的相似一致化,优化计算的时间与空间复杂度,实现用户用电特征准确快速提取,相较常见的K-means和DBSCAN等方法聚类效果更佳,使用逐段聚集平均降维表示,便于后续分析.以某省大工业用户用电数据作为仿真算例进行验证.     

基于概率的反向K最近邻高效查询算法研究

反向K最近邻查询需要确定以给定查询对象作为其k个最近邻之一的所有对象。然而由于大量应用需要处理未知数据,人们迫切需要能够处理未知对象的新算法。这里的主要问题是,一个对象属于RKNN结果集的事件不再是一个确定性事件,而是一个以一定概率成立的随机变量。对基于概率论的未知数据集反向K最近邻(PRKNN)搜索问题展开研究,以足够大的概率返回以查询对象为其最近邻的未知对象。基于一种新的考虑了距离相关性的修剪机制,提出一种PRNN高效查询算法。此外,还给出了如何将该算法扩展至PRKNN(其中k>1)查询处理。最后,将该算法与当前其他最新算法作比较,实验评估结果表明,该算法性能明显优于其他算法。     

基于最近邻聚类的多模型LSSVM逆控制系统

针对逆系统中非线性逆模型辨识困难以及大规模数据采用单模型回归存在精度差和计算量较大的问题,提出了一种基于最近邻聚类的多模型最小二乘支持向量机（LSSVM）逆模型辨识及控制方法。该方法首先使用最近邻聚类算法对数据集做出聚类划分,然后针对每个聚类做最小二乘支持向量回归估计,实现了对系统逆动力学模型的动态辨识。最后将辨识模型作为摔制器模型,与被控对象串联,构成一个动态伪线性对象,从而使非线性对象的控制问题转换为线性对象的控制问题,仿真结果表明基于最近邻聚类的多模型LSSVM逆控制系统辨识能力强,比单模型LSSVM逆摔制系统具有更优的动态跟踪性能,更好的抗干扰能力和鲁棒性。     

Protecting privacy for distance and rank based group nearest neighbor queries

World Wide Web - This paper proposes a novel approach to safeguarding location privacy for GNN (group nearest neighbor) queries. Given the locations of a group of dispersed users, the GNN query...     

IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule

Feature and instance selection are two effective data reduction processes which can be applied to classification tasks obtaining promising results. Although both processes are defined separately, it is possible to apply them simultaneously.This paper proposes an evolutionary model to perform feature and instance selection in nearest neighbor classification. It is based on cooperative coevolution, which has been applied to many computational problems with great success.The proposed approach is compared with a wide range of evolutionary feature and instance selection methods for classification. The results contrasted through non-parametric statistical tests show that our model outperforms previously proposed evolutionary approaches for performing data reduction processes in combination with the nearest neighbor rule.     

Fast and versatile algorithm for nearest neighbor search based on a lower bound tree

In this paper, we present a fast and versatile algorithm which can rapidly perform a variety of nearest neighbor searches. Efficiency improvement is achieved by utilizing the distance lower bound to avoid the calculation of the distance itself if the lower bound is already larger than the global minimum distance. At the preprocessing stage, the proposed algorithm constructs a lower bound tree (LB-tree) by agglomeratively clustering all the sample points to be searched. Given a query point, the lower bound of its distance to each sample point can be calculated by using the internal node of the LB-tree. To reduce the amount of lower bounds actually calculated, the winner-update search strategy is used for traversing the tree. For further efficiency improvement, data transformation can be applied to the sample and the query points. In addition to finding the nearest neighbor, the proposed algorithm can also (i) provide the k-nearest neighbors progressively; (ii) find the nearest neighbors within a specified distance threshold; and (iii) identify neighbors whose distances to the query are sufficiently close to the minimum distance of the nearest neighbor. Our experiments have shown that the proposed algorithm can save substantial computation, particularly when the distance of the query point to its nearest neighbor is relatively small compared with its distance to most other samples (which is the case for many object recognition problems).     

A new nearest neighbor classification method based on fuzzy set theory and aggregation operators

The Fuzzy Nearest Neighbor Classification (FuzzyNNC) has been successfully used, as a tool to deal with supervised classification problems. It has significantly increased the classification accuracy by considering the uncertainty associated with the class labels of the training patterns. Nevertheless, FuzzyNNC's limited methods fail to efficiently handle the imprecision in features measurement and the uncertainty induced by the choice of the distance measure and the number of neighbors in the decision rule. In this paper, we propose a new method called Fuzzy Analogy-based Classification (FABC) to tackle the FuzzyNNC limitations. In this work, we exploit the fuzzy linguistic modeling and approximate reasoning materials in order to endow FABC with intelligent capabilities, like imprecision tolerance, optimization, adaptability and trade-off. Hence, our approach is composed of two main steps. Firstly, we describe the domain features using fuzzy linguistic variables. Secondly, we define the classification process using two intelligent aggregation operators. The first one allows the optimization of the similarity evaluation, by defining the adequate features to be considered. The second one integrates a trade-off strategy within the decision rule, by using a global voting approach with compensation property. The integration of such mechanisms will increase the classification accuracy and make the FuzzyNNC approach more useful for classification problems where imprecision and uncertainty are unavoidable. The proposed FABC is validated on the most known datasets, representing various classification difficulties and compared to the many extensions of the FuzzyNNC approach. The results obtained show that our proposed FABC method can be adapted to different classification problems and improve the classification accuracy. Thus, the FABC has the best rank value against the comparison methods with high significant level. Moreover, we conclude that our optimized similarity and global voting rule are more robust to handle the uncertainty in the classification process than those used by the comparison methods.