A comparative study for content-based dynamic spam classification using four machine learning algorithms

The growth of email users has resulted in the dramatic increasing of the spam emails during the past few years. In this paper, four machine learning algorithms, which are Naïve Bayesian (NB), neural network (NN), support vector machine (SVM) and relevance vector machine (RVM), are proposed for spam classification. An empirical evaluation for them on the benchmark spam filtering corpora is presented. The experiments are performed based on different training set size and extracted feature size. Experimental results show that NN classifier is unsuitable for using alone as a spam rejection tool. Generally, the performances of SVM and RVM classifiers are obviously superior to NB classifier. Compared with SVM, RVM is shown to provide the similar classification result with less relevance vectors and much faster testing time. Despite the slower learning procedure, RVM is more suitable than SVM for spam classification in terms of the applications that require low complexity.     

RVM‐based multi‐class classification of remotely sensed data

The relevance vector machine (RVM), a Bayesian extension of the support vector machine (SVM), has considerable potential for the analysis of remotely sensed data. Here, the RVM is introduced and used to derive a multi‐class classification of land cover with an accuracy of 91.25%, a level comparable to that achieved by a suite of popular image classifiers including the SVM. Critically, however, the output of the RVM includes an estimate of the posterior probability of class membership. This output may be used to illustrate the uncertainty of the class allocations on a per‐case basis and help to identify possible routes to further enhance classification accuracy.     

基于KNN和RVM的分类方法——KNN RVM分类器

针对相关向量机(RVM)算法分类精度低、核参数选择困难等问题,文中提出临界滑动阈值的概念并以其为基础将RVM与K近邻(KNN)算法结合构建分类器——KNN-RVM分类器。从理论上提出并证明KNN-RVM分类过程等价于带软间隔约束的支持向量机的分类过程、KNN-RVM分类器等价于每类只选一个代表点的1-NN分类器、KNN-RVM分类效果优于RVM这3个结论。对这3个不同数据集进行实验证明临界滑动阈值的临界性与滑动性及KNN-RVM分类器的准确性、适应性及全局最优性,提高分类精度,减轻算法对核参数的依赖性,进而证明KNN-RVM分类器是一种有效的分类器。     

On line tool wear monitoring based on auto associative neural network

This paper presents a new tool wear monitoring method based on auto associative neural network. The main advantage of the model lies that it can be built only by the data under normal cutting condition. Therefore, the training samples of the tool wear status are no longer needed during the training process that makes it easier to be applied in real industrial environment than other neural network models. An averaged distance indicator is proposed to denote not only the occurrence of the tool wear but also its severity. Moreover, the Levenberg–Marquardt (LM) training algorithm is introduced to improve the convergence accuracy of the auto associative neural network. Based on the proposed method, a framework for online tool condition monitoring is illustrated and the cutting force data under different tool wear status are collected to simulate the online modeling and monitoring process for the rough and finish milling respectively. The results show that the proposed indicator can reflect the evolution process of tool wear correctly and the LM algorithm is more accurate in comparison with the gradient descent methods. Therefore, it casts new light on practical application of neural network in the field of on line tool condition monitoring.     

基于SVM 的二叉树多类分类算法及其在故障诊断中的应用

基于结构风险最小化原则的支持向量机(SVM)对小样本决策具有较好的学习推广性。但由于常规SVM算法是从2类分类问题推导出的，在解决故障诊断这种典型的多类分类问题时存在因雄，因而提出一种依赖故障优先级的基于SVM的二叉树多级分类器实现(2PTMC)方法，该方法具有简单、直观，重复训练样本少的优点。通过将其应用于柴油机振动信号的故障诊断，获得了令人满意的效果。     

Underwater bottom still mine classification using robust time–frequency feature and relevance vector machine

A novel approach to the problem of detecting and classifying underwater bottom mine objects in littoral environments from acoustic backscattered signals is considered. We begin by defining robust short-time Fourier transform to convert the received echo into a time–frequency (TF) plane. Identify interest local region in spectrogram, then features in TF plane with robustness to reverberation and noise disturbances are built. Finally, echo features are sent to a relevance vector machine (RVM) classifier that represents a Bayesian extension of support vector machine (SVM). To evaluate the performace of the classifier based on this approach, the classification experiment of two typical types of mines lying on the bottom have been performed with a broad bandwidth active sonar. Each of the targets was lying on the lake bottom at a depth of 20 m. The case study exploits the robustness of a feature extraction scheme, and furthermore, RVM yields a much sparser solution and improves the classification accuracy than SVM in an impulse noise environment.     

基于模糊SVM模型的入侵检测分类算法

为解决入侵检测分类遇到的训练样本数量少、分类准确率低的问题,提出基于模糊支持向量机的多级分类机制。该分类机制训练模糊SVM模型将数据粗分为正常与攻击大类,采用DBSCAN算法产生细分模型进行攻击子集的自动聚类,将有关数据细分得到攻击的具体细类。在机制设计中,优化了隶属度函数的计算、设计了数据标准化与归一化等过程,并训练了高效分类器。实验表明,针对网络入侵检测数据中常见的孤立点干扰、噪声多,并且负样本占比多的网络业务数据集,新算法在保持分类准确率高的前提下,分类过程的计算时间较短。     

相关向量机在车型识别中的应用研究

相关向量机是一种稀疏的贝叶斯学习算法,对非线性、高维数的小样本问题有非常好的分类效果和学习推广能力.而且使用较少的核函数,研究了用相关向量机技术进行车型识别,设计了基于相关向量机的车型分类器.实验结果表明,基于相关向量机的车型分类器不仅具有基于支持向量机的车型分类器的相同性能,而且比支持向量机使用更少的核函数,实验取得了较好的分类效果.     

一种基于变分相关向量机的特征选择和分类结合方法

相关向量机(Relevance vector machine, RVM)是一种函数形式等价于支持向量机(Support vector machine, SVM)的全概率模型,利用变分贝叶斯(Variational Bayesian, VB)方法求解的RVM可以给出所有参数的后验分布. 进一步,通过对样本所在原始特征空间的稀疏化,基于线性核的RVM可以在分类的同时实现对原始特征的线性选择. 本文在传统VB-RVM的基础上提出一种特征选择和分类结合方法. 该方法采用Probit模型将分类问题与回归问题有机地结合起来, 同时,通过对特征维的幂变换扩展,不仅在分类时增加了样本的信息量, 可以构造非线性分类面,而且实现了非线性特征选择的功能. 通过对仿真数据和实测数据分别进行实验, 证明了该特征选择和分类结合方法的实用性和有效性.     

基于ELMC的蛋白质折叠识别方法

传统的机器学习方法在处理蛋白质折叠类型识别问题时需要花费大量的时间来调节最佳参数,利用一种新的极限学习机（Extreme Learning Machine,ELM）分类优化方法（Extreme Learning Machine for Classification,ELMC）对蛋白质折叠进行识别,仅需调节很少的参数值就可达到很好的测试精度。与支持向量机（Support Vector Machine,SVM）和推荐相关向量机（Relevance Vector Machine,RVM）相比,ELMC能获得更好的泛化性能,而且在寻找最优解的训练时间比较上,ELMC比SVM平均要快35倍,比RVM要快12倍。     

基于LASVM-NC和TF.RF的文本分类方法

非凸在线支持向量机（LASVM-NC）具有抗噪能力强和训练速度快的优点,而词频相关频率积（tf.rf）则是一种自适应能力很强、分类性能非常好的文本特征。通过把非凸在线支持向量机和词频相关频率积相结合,提出了一种新的文本分类方法,即LASVM-NC＋tf.rf。实验结果表明,这种方法在LASVM-NC与多种其他特征的结合中性能是最好的,且与SVM＋tf.rf相比,不仅所产生的分类器具有泛化能力更强、模型表达更稀疏的优点,而且在处理含噪声的数据时具有更好的鲁棒性,在处理大规模数据时具有快得多的训练速度。     

基于SVM-KNN的半监督托攻击检测方法

针对支持向量机方法在标记用户数据不充分的情况下无法有效实现托攻击检测的不足,提出一种基于SVM-KNN的半监督托攻击检测方法。根据少量标记用户数据训练一个初始SVM分类器,利用初始SVM对大量未标记用户数据进行分类,挑选出分类边界附近有可能成为支持向量的样本点,利用KNN分类器优化边界向量的标记质量,再将重新标注过的边界向量融入训练集,迭代训练逐步改善SVM的分类边界,最终获得系统决策函数。实验结果表明在标记用户数据较少的情况下,方法能有效提高托攻击的检测精度和效率,具有较强的推广能力。     

高光谱影像的BDT-SVM地物分类算法与应用

面对海量数据的特征空间高维性及训练样本的有限性,高光谱遥感影像若采用常规统计模式的分类方法难以获得较好的分类结果。因此探讨支持向量机(SVM)分类器的基本原理,针对EO-1Hyperion高光谱影像的分类特点及现有多类SVM算法所存在的训练时间长及分类精度低等问题,引入二叉决策树SVM(BDT-SVM)分类算法,并提出一种新的类间分离度定义方法及相应的客观确定二叉树结构的策略,由此生成改进的BDT-SVM算法。实验结果表明:与其他多类分类方法相比,基于改进的BDT-SVM算法的高光谱影像地物分类效果更好,总体精度达到90.96%,Kappa系数为0.89,该算法还解决了经典SVM多类分类可能存在的不可分区域问题。     

基于支持向量机的枪弹外观缺陷识别与分类

为解决枪弹外观缺陷自动分类问题,提出了一种基于支持向量机的枪弹外观缺陷自动识别与分类模型。首先针对枪弹表面缺陷的图像特点,从几何、灰度、纹理三方面进行了特征提取,在此基础上建立了基于支持向量机的枪弹外观缺陷分类模型,并对特征参数进行了优选;研究了支持向量机中惩罚系数和核函数参数对分类器性能的影响;通过实验与基于BP神经网络的枪弹外观缺陷分类器进行了比较,结果表明,在小样本下,基于支持向量机的枪弹外观缺陷分类器性能更好。     

Tool wear state recognition based on linear chain conditional random field model

Tool condition monitoring (TCM) system is paramount for guaranteeing the quality of workpiece and improving the efficiency of the machining process. To overcome the shortcomings of Hidden Markov Model (HMM) and improve the accuracy of tool wear recognition, a linear chain conditional random field (CRF) model is presented. As a global conditional probability model, the main characteristic of this method is that the estimation of the model parameters depends not only on the current feature vectors but also on the context information in the training data. Therefore, it can depict the interrelationship between the feature vectors and the tool wear states accurately. To test the effectiveness of the proposed method, acoustic emission data are collected under four kinds of tool wear state and seven statistical features are selected to realize the tool wear classification by using CRF and hidden Markov model (HMM) based pattern recognition method respectively. Moreover, k-fold cross validation method is utilized to estimate the generation error accurately. The analysis and comparison under different folds schemes show that the CRF model is more accurate for the classification of the tool wear state. Moreover, the stability and the training speed of the CRF classifier outperform the HMM model. This method casts some new lights on the tool wear monitoring especially in the real industrial environment.     

基于QPSO训练的SVM核函数集成学习研究

针对训练子集随机性强、规模大、算法时空复杂度高等问题,提出了基于量子微粒群的支持向量机（QPSO-SVM）核函数集成学习算法。该方法首先采用K-Means算法对训练样本进行聚类分析,然后根据其聚类分布选择少量具有代表性的样本,并通过基于量子行为的粒子群算法来训练单个支持向量机（SVM）,最后通过贝叶斯投票方法得到集成的SVM分类学习器。实验表明该方法在非线性高复杂度的数据分类中对分类精度有较大提高。     

基于云PSO 的RVM入侵检测

入侵检测可为计算机网络信息提供安全保障,在其方法研究中,由于相关向量机(RVM)具有高稀疏性且预测中使用概率因素,在网络入侵检测中优于支持向量机.然而RVM的核函数参数是经验估计的,为此,提出一种基于云模型的粒子群优化算法的RVM方法,即采用云粒子群算法确定RVM的核参数,构建RVM分类模型,再采用一对一分类方法进行多类检测分类.经入侵检测实验研究,所得结果表明所提出的方法优于基于常规相关向量机的检测方法,且具有更高的入侵检测精度.     

A Two Stage Sequential Ensemble Applied to the Classification of Alzheimer’s Disease Based on MRI Features

We present a two stage sequential ensemble where data samples whose output from the first classifier fall in a low confidence output interval (LCOI) are processed by a second stage classifier. Training is composed of three processes: training the first classifier, determining the LCOI of the first classifier, and training the second classifier upon the data items whose output fall in the LCOI. The LCOI is determined varying a threshold on the false positive rate (FPR) and false negative rate (FNR) curves. We have tested the approach on a database of feature vectors for the classification of Alzheimer’s disease (AD) and control subjects extracted from structural magnetic resonance imaging (sMRI) data. In this paper, we focus on the combinations obtained when the first classifier is a relevance vector machine (RVM). Obtained results improve over previous results for this database.     

基于改进S 变换和贝叶斯相关向量机的电能质量扰动识别

提出一种改进S变换和相关向量机相结合的电能质量扰动分类法.首先通过引入调节因子构建时频分辨率可控的改进S变换,从而提取各类扰动信号的时频特性;然后利用层次分类法与最小输出编码法构建贝叶斯相关向量机多级分类树模型,实现电能质量扰动信号的分类与识别.研究表明,该方法能在强噪声背景下获得高精度的扰动分类识别率,具备比S变换更高的时频分析能力,较支持向量机需要更少的相关向量数目,测试时间更短.     

基于遗传算法优化SVM的嵌入式网络系统异常入侵检测

由于传统嵌入式网络系统入侵检测方法难以获得较高的检测精度,提出基于遗传算法优化的支持向量机(GA-SVM)的网络入侵检测技术.支持向量机分类器能够较好地解决少样本、高维、非线性分类问题.然而,支持向量机训练参数的选择对其分类精度有着很大影响,遗传算法能够同时优化支持向量机的训练参数,采用遗传算法进行支持向量机的训练参数同步优化.实验结果表明,这种遗传算法优化的支持向量机分类入侵检测模型有着很高的检测精度.