基于PCA+KNN和kernal-PCA+KNN算法的废旧纺织物鉴别

该研究采集了15类废旧纺织物的4 998张近红外谱图,以7∶3的比例分为训练集和验证集,并分别采用主成分分析(PCA)与核主成分分析(kernal-PCA)两种不同降维方法对数据进行降维,并选用余弦相似度(cosine)核作为kernal-PCA的最佳核函数,最后分别将PCA和kernal-PCA降维处理后的数据进行k-近邻算法(KNN)训练。结果表明,kernal-PCA+KNN的模型准确率(95.17%)优于PCA+KNN模型的准确率(92.34%)。研究表明,kernal-PCA+KNN算法可以实现15类废旧纺织物识别准确率的提升,为废旧纺织物在线近红外自动分拣提供有力的技术支撑。

基于KNN算法的改进的一对多SVM多分类器

针对传统支持向量机(SVM)多分类一对多算法存在的运算量大、耗时长、数据偏斜以及对最优超平面附近点分类易出错问题,提出了一种改进方法。将数据空间分为密集区和稀疏区,各类中密集点归于密集区,其余归于稀疏区。将每类中密集点连同它附近的点用于训练得到相应的SVM分类器。在测试阶段,对密集区的待测样本用传统的一对多判别准则来做类别预测;对稀疏区的待测样本则采用K近邻(KNN)算法。数值实验结果表明,改进的算法在耗时和分类精度上都优于原算法,对解决一对多算法存在的问题有较好的成效。

基于小波分析及KNN的民族文字分类方法

提出一种基于小波分析的少数民族文字文字分类识别方法.该方法采用多辨识小波分解,从而获得小波能量和小波能量比例分布的特征描述,结合少数民族文字文本图片的纹理特征,选择加权KNN分类器.实验证明:该识别方法对藏文、西双版纳傣文、纳西象形文、维吾尔文、德宏傣文和彝文6种常用的少数民族文字及汉字、英语共8种文字的分类测试达到96%的识别效果.

一种新的中文文本分类算法——One Class SVM-KNN算法

中文文本分类在数据库及搜索引擎中得到广泛的应用,K-近邻(KNN)算法是常用于中文文本分类中的分类方法,但K-近邻在分类过程中需要存储所有的训练样本,并且直到待测样本需要分类时才建立分类,而且还存在类倾斜现象以及存储和计算的开销大等缺陷。单类SVM对只有一类的分类问题具有很好的效果,但不适用于多类分类问题,因此针对KNN存在的缺陷及单类SVM的特点提出One Class SVM-KNN算法,并给出了算法的定义及详细分析。通过实验证明此方法很好地克服了KNN算法的缺陷,并且查全率、查准率明显优于K-近邻算法。

基于熵降噪优化相似性距离的KNN算法研究

围绕KNN算法,以寻求高精度、高效率的相似性距离度量方法为主要研究目的。根据特征参数熵变换指标的类别特点,提出一种运用熵特征变换指标设计相互类别差异量的相似性距离度量,以降低特征参数类别噪音。对熵降噪优化、熵相关度差异、类可信度计算、传统欧式距离及相同特征参数几种KNN算法进行理论分析、Letter和Pima Indians Diabetes数据集仿真实验及KDD CUP'99的实际应用,均显示该算法在KNN算法中具有很好的效果。

Study on Chironomid Larvae Recognition Based on DWT and Improved KNN

A chironomid larvae images recognition method based on wavelet energy feature and improved KNN is developed. Wavelet decomposition and color information entropy are selected to construct vectors for KNN that is used to classify of the images. The distance function is modified according to the weight determined by the correlation degree between feature and class, which effectively improves classification accuracy. The result shows the mean accuracy of classification rate is up to 95.41% for freshwater plankton images, such as chironomid larvae, cyclops and harpacticoida.

双极隶属度F_KNN分类算法在算法管理中的应用

算法管理是信息融合领域中一个新的研究课题,其过程涉及到对信息融合数据源的分类问题。针对信息融合数据源样本的特征,提出一种双极隶属度F_KNN分类算法,通过邻居质量优先隶属度函数和双极隶属度判决规则,得到理想的数据源分类结果。

基于三支决策的KNN增量算法

k-近邻(k-Nearest Neighbour,KNN)算法是一种有效的多分类算法,他具有简单、稳定的特点,在数据挖掘领域得到了广泛的应用。但是有2个主要缺点,一是算法的准确度与k值有很大关系,不同的k值会导致准确率有很大的不同;二是他属于非增量式算法,随着数据量的增加,算法的分类速度会越来越慢,影响他在海量数据分析中的应用。三支决策的主要思想是将整体分成3个独立的部分,引入了不承诺的决策选项,规避了错误接受或者错误拒绝的损失。把三支决策思想引入KNN算法,对边界域样本特殊处理,会减小分类代价,提高海量数据处理的正确性,同时对KNN算法进行改进,提出了一种基于三枝决策的KNN增量式算法,提高了原有算法的快速性。

不均衡训练集下短信过滤系统kNN方法的研究

不良短信的泛滥,严重影响了社会风气,干扰了人们正常的生活秩序,研发不良短信过滤技术具有相当的实用价值。应用中科院计算所研制开发的ICTCLAS分词系统,结合TFIDF词权度量指标提取关键词,实现短信文本到特征向量的转换,然后采用kNN方法实现短信的类别判断,从而实现不良短信的过滤。另外,针对训练集分布不均衡的情况,应用基于密度的改进方法,较为有效地处理了原来分类结果倾向于大类别样本的情况。实验表明,改进后的方法的准确率约79.18%,比原方法提升了约1.23%。该方法能够比较有效地过滤不良短信,具有一定的实用价值。

基于聚类算法的KNN文本分类算法研究

KNN算法是一种在人工智能领域如专家系统、数据挖掘、模式识别等方面广泛应用的算法。该算法简单有效,易于实现。但是KNN算法在决定测试样本的类别时,是把所求的该测试样本的K个最近邻是等同看待的,即不考虑这K个最近邻能表达所属类别的程度。由于训练样本的分布是不均匀的,每个样本对分类的贡献也就不一样,因此有必要有区别的对待训练样本集合中的每个样本。利用聚类算法,求出训练样本集合中每个训练样本的隶属度,利用隶属度来区别对待测试样本的K个最近邻。通过实验证明,改进后的KNN算法较好的精确性。

A KNN-based two-step fuzzy clustering weighted algorithm for WLAN indoor positioning

Although k-nearest neighbors （KNN） is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means （FCM） clustering algorithm is applied to improve it. Thus, a KNN-based two-step FCM weighted （KTFW） algorithm for indoor positioning in wireless local area networks （WLAN） is presented in this paper. In KTFW algorithm, k reference points （RPs） chosen by KNN are clustered through FCM based on received signal strength （RSS） and location coordinates. The right clusters are chosen according to rules, so three sets of RPs are formed including the set of k RPs chosen by KNN and are given different weights. RPs supposed to have better contribution to positioning accuracy are given larger weights to improve the positioning accuracy. Simulation results indicate that KTFW generally outperforms KNN and its complexity is greatly reduced through providing initial clustering centers for FCM.

Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor

On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.

Fault Diagnosis in Robot Manipulators Using SVM and KNN

In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work.For both classifiers,the torque,the position and the speed of the manipulator have been employed as the input vector.However,it is to mention that a large database is needed and used for the training and testing phases.The SVM method used in this paper is based on the Gaussian kernel with the parametersγand the penalty margin parameter“C”,which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis.Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm(SCARA)robot manipulator,and the results showed that the Particle Swarm Optimization(PSO)increased the per-formance of the SVM algorithm with the 96.95%accuracy while the KNN algo-rithm achieved a correlation up to 94.62%.These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.

Diagnosis of Disc Space Variation Fault Degree of Transformer Winding Based on K-Nearest Neighbor Algorithm

Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding.

Characteristics,classification and KNN-based evaluation of paleokarst carbonate reservoirs:A case study of Feixianguan Formation in northeastern Sichuan Basin,China

The Feixianguan Formation reservoirs in northeastern Sichuan are mainly a suite of carbonate platform deposits.The reservoir types are diverse with high heterogeneity and complex genetic mechanisms.Pores,vugs and fractures of different genetic mechanisms and scales are often developed in association,and it is difficult to classify reservoir types merely based on static data such as outcrop observation,and cores and logging data.In the study,the reservoirs in the Feixianguan Formation are grouped into five types by combining dynamic and static data,that is,karst breccia-residual vuggy type,solution-enhanced vuggy type,fractured-vuggy type,fractured type and matrix type(non-reservoir).Based on conventional logging data,core data and formation microscanner image(FMI)data of the Qilibei block,northeastern Sichuan Basin,the reservoirs are classified in accordance with fracture-vug matching relationship.Based on the principle of cluster analysis,K-Nearest Neighbor(KNN)classification templates are established,and the applicability of the model is verified by using the reservoir data from wells uninvolved in modeling.Following the analysis of the results of reservoir type discrimination and the production of corresponding reservoir intervals,the contributions of various reservoir types to production are evaluated and the reliability of reservoir type classification is verified.The results show that the solution-enhanced vuggy type is of high-quality sweet spot reservoir in the study area with good physical property and high gas production,followed by the fractured-vuggy type,and the fractured and karst breccia-residual vuggy types are the least promising.

Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.

基于证据理论的多分类器中文微博观点句识别

随着新技术及社会网络的发展与普及,微博用户数据量剧增,与此相关的研究引起了学术界和工业界的关注。针对中文微博语句特点,通过对比多种特征选取方法,提出一种新的特征统计方法。根据构建的词语字典与词性字典,分析支持向量机、朴素贝叶斯、K最近邻等分类模型,并利用证据理论结合多分类器对中文微博观点句进行识别。采用中国计算机学会自然语言处理与中文计算会议(NLP&CC 2012)提供的数据,运用该方法得到的准确率、召回率和F值分别为70.6%、89.2%、78.9%,而NLP&CC2012公布的评测结果相应平均值分别为72.7%、61.5%、64.7%,该方法在召回率和F值2个指标上超过其平均值,而F值比NLP&CC2012评测结果的最好值高出0.5%。

云计算中保护数据隐私的快速多关键词语义排序搜索方案

可搜索加密技术主要解决在云服务器不完全可信的情况下,支持用户在密文上进行搜索.该文提出了一种快速的多关键词语义排序搜索方案.首先,该文首次将域加权评分的概念引入文档的评分当中,对标题、摘要等不同域中的关键词赋予不同的权重加以区分.其次,对检索关键词进行语义拓展,计算语义相似度,将语义相似度、域加权评分和相关度分数三者结合,构造了更加准确的文档索引.然后,针对现有的MRSE(Multi-keyword Ranked Search over Encrypted cloud data)方案效率不高的缺陷,将创建的文档向量分块,生成维数较小的标记向量.通过对文档标记向量和查询标记向量的匹配,有效地过滤了大量的无关文档,减少了计算文档相关度分数和排序的时间,提高了搜索的效率.最后,在加密文档向量时,将文档向量分段,每一段与对应维度的矩阵相乘,使得构建索引的时间减少,进一步提高了方案的效率.理论分析和实验结果表明:该方案实现了快速的多关键词语义模糊排序搜索,在保障数据隐私安全的同时,有效地提高了检索效率,减少了创建索引的时间,并返回更加满足用户需求的排序结果.

一种改进的结合K近邻法的SVM分类算法

在对支持向量机在超平面附近容易对测试样本造成错分进行研究的基础上,改进了将支持向量机分类和k近邻分类相结合的方法,形成了一种新的分类器。在分类阶段计算待识别样本和最优分类超平面的距离,如果距离差大于给定阈值可直接应用支持向量机分类,否则用最佳距离k近邻分类。数值实验表明,使用支持向量机结合最近邻分类的分类器分类比单独使用支持向量机分类具有更高的分类准确率。

基于AdaBoost的雷达目标HRRP识别

针对雷达高分辨一维距离像(HRRP)特征维数高的特点,采用线性判别分析(LDA)和核Fisher判别分析(KFD)方法进行特征压缩和提取。分析了基于AdaBoost算法的分类器的设计思想和实现步骤。构造了高斯型弱分类器,利用AdaBoost算法集成高斯弱分类器实现了一强分类器,利用此分类器对降维后的HRRP数据进行分类识别,并同K近邻分类器(KNN)、支持向量机(SVM)分类器进行比较,得到一些有价值的结论。