粗糙集属性约简的极限学习机网络入侵检测算法

针对网络入侵数据量大、属性冗余及属性之间线性相关导致分类算法计算速度慢、准确度不高等问题,提出一种改进粗糙集属性约简的极限学习机网络入侵分类算法。对训练集采用粗糙集正域和分辨矩阵相结合的方法获得属性核,筛选出只有属性核的数据集得到无冗余属性的特征集合;使用极限学习机(ELM)作为分类模型进行分类,使用支持向量机(SVM)、神经网络、极限学习机比较证明提出方法的有效性,为网络入侵检测提供一种新的解决方法。     

基于特征选择和支持向量机的异常检测方法

针对异常检测系统虚警率高、检测率低以及冗余特征对检测系统造成负担的问题,提出一种基于特征选择和支持向量机相结合的异常检测方法.该方法通过构造一种基于分类模型分类准确率计算的特征选择算法,筛选出能够获得分类准确率最高的特征组合,并与支持向量机分类算法相结合,实现数据的异常检测.仿真测试结果表明,该方法具有较高的检测准确率和较低的检测时间,并通过去除噪声特征,降低了系统的数据处理难度.     

基于随机最小冗余条件互信息和支持向量机的混合入侵检测特征选择

针对入侵检测日志中存在着大量的不相关和冗余特征属性,严重影响检测的实时性,而大多数特征选择算法不能兼顾相关性和信息量,且容易陷入局部最优解,提出一种基于随机最小冗余条件互信息和支持向量机的混合入侵检测特征选择方法。首先利用互信息和相关性去除没有分类信息量和特征间高度相关的冗余特征,然后利用随机最小冗余条件互信息准则以及支持向量机选择出具有最多分类信息量的最优特征子集,一定程度地避免了局部最优解。实验结果表明,该方法能在确保入侵检测正确率的情况下以更高的效率选择出最小最优的入侵检测特征子集。     

基于最大间隔最小体积超球支持向量机的多主题分类算法

针对多主题分类,结合最大间隔最小体积超球支持向量机和模糊理论,提出一种多主题最大间隔最小体积超球支持向量机来实现多主题分类.该算法首先基于最大间隔最小体积超球支持向量机,采用1-a-r方法训练子分类器,通过子分类器得到待分类样本的隶属度向量,再依据隶属度向量判定该待分类样本所属类别.实验结果表明,该算法具有较好的准确率、召回率、F1值.     

基于小波变换和AdaBoost极限学习机的癫痫脑电信号分类

针对单一极限学习机(ELM)在癫痫脑电信号研究中分类结果不稳定、泛化能力差的缺陷,提出一种基于互信息(MI)的AdaBoost极限学习机分类算法。该算法将AdaBoost引入到极限学习机中,并嵌入互信息输入变量选择,以强学习器最终的性能作为评价指标,实现对输入变量以及网络模型的优化。利用小波变换(WT)提取脑电信号特征,并结合提出的分类算法对UCI脑电数据集以及波恩大学癫痫脑电数据进行分类。实验结果表明,所提方法相比传统方法以及其他同类型研究,在分类精度和稳定性上有着明显提高,并具有较好的泛化性能。     

基于粗糙集与改进LSSVM的入侵检测算法研究

提出了基于粗糙集和改进最小二乘支持向量机的入侵检测算法。算法利用粗糙集理论的可辨识矩阵对样本属性进行约简,减少样本维数;利用稀疏化算法对最小二乘支持向量机进行改进,使其既具备稀疏化特性又具备快速检测的特点,提高了数据样本分类的准确性。结合算法不仅充分发挥粗糙集对数据有效约简和支持向量机准确分类的优点,同时克服了粗糙集在噪声环境中泛化性较差,支持向量机识别有效数据和冗余数据的局限性。通过实验证明,基于粗糙集和改进最小二乘支持向量机的入侵检测算法的检测精度高,误报率和漏报率较低,检测时间短,验证了算法的实效性。     

基于ELM和SVM皮电信号情绪分类识别的研究

皮电信号(Electrodermal activity,EDA)是一种不平稳的非周期性微弱信号，能够反映不同情绪状态下人体皮肤内血管的舒张和收缩以及汗腺的分泌活动，在情绪分类识别中具有重要的研究意义。针对EDA信号时域特征提取的分类方法识别率低，极限学习机(Extreme learning machine, ELM)具有训练参数少、学习效率高、泛化能力强等的优点，本研究基于生理信号采集设备采集了12位被试的皮电数据，从时域上进行特征提取，输入KS(Kennard-Stone)模型随机筛选样本，使用支持向量机(Support Vector Machine, SVM)和极限学习机的方法对积极、消极、中性情绪进行分类，并对分类准确率进行比较。实验结果表明，相较于支持向量机分类器55.56%的平均分类准确率，极限学习机平均分类准确率达64.16%,提高了8.6%,采用配对t检验进行验证，t检验结果为P=0.047 781 453<0.05,具有显著的统计学差异。极限学习机算法适用于情绪分类识别，相较于支持向量机具有更好的情感识别效果。     

基于多任务支持向量机的能源互联网数据深度融合方法

在多任务环境下，能源互联网数据冗余干扰较强，后期数据处理误差较大，故提出一种基于多任务支持向量机的能源互联网数据深度融合方法。数据层融合接收待融合能源互联网数据后实施数据离散化、矩阵化处理，将处理后数据发送至特征层融合内，特征层融合利用互信息变量选择算法，提取数据分类特征，并将所提取特征作为决策层融合多任务支持向量机输入，多任务支持向量机将支持向量机各数据分组视为子任务，运算过程中需保证全部学习机间全局差异最小化实现子学习机局部最优化，多任务支持向量机实施数据决策级融合后输出最终融合结果。实验结果表明，该方法准确率高于99%,且融合加入10 dB白噪声的能源互联网数据最小均方误差仍低于1%。     

基于脑电信号的情感识别研究

针对如何提高脑电信号情感识别的正确率这一问题,在得到的原始脑电信号进行分频带特征提取后,一方面采用支持向量机、K近邻算法、朴素贝叶斯和神经网络算法对小波熵、近似熵、功率谱密度、微分熵,进行训练和分类学习;另一方面,基于四种不同的电极放置方式,对微分熵特征采用支持向量机和经遗传算法参数寻优的支持向量机算法进行训练。结果显示,在12通道条件下能够得到91.99%的总体准确率,最高情感识别准确率已经达到97.59%。研究结果表明,减少电极可以获得较高的情感识别分类结果,并且采用参数寻优后的支持向量机算法能够有效提升准确率。     

基于极限学习机的焊点质量检测

焊点加工直接影响电子产品的可靠性,焊点的检测对产品质量的提高尤为重要。应用主成分分析与极限学习机对焊点质量进行检测。首先通过中值滤波和分水岭算法对焊点图像进行预处理,得到焊点轮廓及区域划分情况并用主成分分析法进行降维;然后采用200个隐含层网络节点、sigmoid响应函数的极限学习机算法对预处理结果进行分类。测试结果表明,极限学习机算法能够对焊点精确分类,与支持向量机、邻近算法、卷积神经网络相比,取得更高的检测准确率,检测时间更短。     

基于局部模式的癫痫脑电信号自动分类方法

为有效地检测脑电图(EEG)中的癫痫信号,设计一维局部三值模式(1D-LTP)算子提取信号特征,并结合主成分分析(PCA)和极限学习机(ELM)对特征进行分类。通过1D-LTP算子计算信号点的顶层模式和底层模式下的特征变换码以准确滤除干扰信号,并对变换码直方图PCA降维后采用ELM进行分类,以10折交叉验证评估分类性能。实验结果表明,该方法能有效识别在癫痫发作期的EEG信号,其准确率可达99.79%。     

Comparative Study of ELM and SVM in Hyperspectral Image Supervision Classification

Combining the spatial features and spectral feature of hyperspectral remote sensing image in supervised classification can effectively improve the classification time and accuracy.In this study,the spatial information extraction method,named watershed transform,was combined with the Extreme Learning Machine(ELM)and Support Vector Machine(SVM)methods.The classification results of the datasets with the spatial features and without the spatial features were synthetically evaluated and compared.Two hyperspectral datasets,the ROSIS data of Pavia university and the Hyperion data of Okavango Delta(Botswana),were selected to test the methods.After preprocessing,the training samples were selected from the images as the reference areas for each type,and the spectral features of each type were analyzed.The two classification methods were utilized to classify the hyperspectral datasets and relevant classification results were obtained.based on the validation samples selected from the images,the classification results were evaluated using the confusion matrix and the execution times.After that,the spectral features and spatial features were combined to classify the data.The results show that the Extreme Learning Machine(ELM) is superior to the Support Vector Machine(SVM)in the classification time and precision,and the spatial features are introduced in the classification process,which can effectively improve the classification accuracy.     

蝙蝠算法优化极限学习机的滚动轴承故障分类

针对传统智能故障诊断方法在滚动轴承的故障诊断中诊断准确率不高的问题,引入了一种启发式搜索算法——蝙蝠算法(BA)优化极限学习机(ELM)的方法,利用ELM构建滚动轴承故障诊断分类模型。首先采用滚动轴承振动信号的五种代表性时域无量纲指标作为诊断模型输入特征,然后,利用蝙蝠算法的全局寻优能力对ELM模型的参数进行优化,获取最优输入权重和隐含层偏置的ELM分类模型,最后采用美国西储大学轴承数据中心网站公开发布的轴承探伤数据集验证算法诊断效果。实验结果表明：该方法可以有效地对滚动轴承不同故障状态进行识别,与BP神经网络、支持向量机(SVM)和极限学习机(ELM)方法比较,所提出的方法能够提高故障诊断准确率,达到99.17%。     

Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data

The objective is to develop a non-invasive automatic method for detection of epileptic seizures with motor manifestations. Ten healthy subjects who simulated seizures and one patient participated in the study. Surface electromyography (sEMG) and motion sensor features were extracted as energy measures of reconstructed sub-bands from the discrete wavelet transformation (DWT) and the wavelet packet transformation (WPT). Based on the extracted features all data segments were classified using a support vector machine (SVM) algorithm as simulated seizure or normal activity. A case study of the seizure from the patient showed that the simulated seizures were visually similar to the epileptic one. The multi-modal intelligent seizure acquisition (MISA) system showed high sensitivity, short detection latency and low false detection rate. The results showed superiority of the multi-modal detection system compared to the uni-modal one. The presented system has a promising potential for seizure detection based on multi-modal data.     

Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines

The electroencephalogram (EEG) has proven a valuable tool in the study and detection of epilepsy. This paper investigates for the first time the use of Permutation Entropy (PE) as a feature for automated epileptic seizure detection. A Support Vector Machine (SVM) is used to classify segments of normal and epileptic EEG based on PE values. The proposed system utilizes the fact that the EEG during epileptic seizures is characterized by lower PE than normal EEG. It is shown that average sensitivity of 94.38% and average specificity of 93.23% is obtained by using PE as a feature to characterize epileptic and seizure-free EEG, while 100% sensitivity and specificity were also obtained in single-trial classifications.     

Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification

EEG signal analysis involves multi-frequency non-stationary brain waves from multiple channels. Segmenting these signals, extracting features to obtain the important properties of the signal and classification are key aspects of detecting epileptic seizures. Despite the introduction of several techniques, it is very challenging when multiple EEG channels are involved. When many channels exist, a spatial filter is required to eliminate noise and extract relevant information. This adds a new dimension of complexity to the frequency feature space. In order to stabilize the classifier of the channels, feature selection is very important. Furthermore, and to improve the performance of a classifier, more data is required from EEG channels for complex problems. The increase of such data poses some challenges as it becomes difficult to identify the subject dependent bands when the channels increase. Hence, an automated process is required for such identification.The proposed approach in this work tends to tackle the multiple EEG channels problem by segmenting the EEG signals in the frequency domain based on changing spikes rather than the traditional time based windowing approach. While to reduce the overall dimensionality and preserve the class-dependent features an optimization approach is used. This process of selecting an optimal feature subset is an optimization problem. Thus, we propose an adaptive multi-parent crossover Genetic Algorithm (GA) for optimizing the features used in classifying epileptic seizures. The GA-based approach is used to optimize the various features obtained. It encodes the temporal and spatial filter estimates and optimize the feature selection with respect to the classification error. The classification was done using a Support Vector Machine (SVM).The proposed technique was evaluated using the publicly available epileptic seizure data from the machine learning repository of the UCI center for machine learning and intelligent systems. The proposed approach outperforms other ones and achieved a high level of accuracy. These results, indicate the ability of a multi-parent crossover GA in optimizing the feature selection process in EEG classification.     

基于深度自编码网络的高校招生咨询算法

高校招生在线咨询通常采用人工回复或基于关键词匹配的问答系统来处理,常存在人工回复效率低下,问答系统答非所问的问题;此外,咨询文本往往比较简短,文本向量化表示易导致高维稀疏问题。针对上述问题,提出一种基于栈式降噪稀疏自编码网络（SDSAE）的招生咨询算法。首先,利用自编码网络对短文本进行特征提取和降维,引入数据集增强技术和添加噪声技术解决训练样本规模较小且分类不均问题,提高算法的泛化能力;获得短文本低维特征表示后,结合反向传播（BP）算法对文本进行分类。所提算法分类效果优于BP、支持向量机（SVM）、极限学习机（ELM）等算法,能显著提高招生咨询文本的分类效果。     

基于随机森林的跌倒检测算法

针对现有跌倒检测算法由于缺乏真实老人跌倒样本以及使用年轻人仿真跌倒样本规模较小导致的过拟合和适应性不足等问题,提出了基于随机森林的跌倒检测算法.该算法采用滑动窗口机制,对窗口内的加速度数据进行时间域和变换域处理,提取时间域和变换域特征参数后,在所有样本集中进行有放回的Bootstrap随机抽样和属性随机选择,构建多个基于最佳属性分割的支持向量机(SVM)基本分类器.在线跌倒检测阶段,对多个SVM基本分类器的分类结果采用少数服从多数的原则,给出最终判定结果.实验表明,随机森林跌倒检测算法可获得95.2%的准确率、90.6%的敏感度和93.5%的特异性,明显优于基于SVM和反向传播(BP)神经网络跌倒检测算法,反映出随机森林跌倒检测算法能更准确地检测跌倒行为,具有较强的泛化能力和鲁棒性.     

利用多源信息和极限学习机的人体运动意图识别

快速准确的步态识别是实现智能假肢灵活控制的基础与前提,步态(平地行走、上下楼梯和上下坡)的有效识别是关键.为了克服由单一信息源无法辨识复杂多步态的难题,搭建人体步态多源运动信息系统获取髋关节角度信号、加速度信号和足底压力信号,利用足底压力信号将人体步态划分为4个片段,并根据人体步态的特点确定了4个片段下髋关节角度、髋关节加速度信号的特征值,采用核主成分分析(KPCA)对原始特征的组合进行融合,得到信息互补的特征值,最后利用极限学习机(ELM)进行识别,实验结果表明该方法对平地行走、上楼、下楼、上坡、下坡5种步态的平均识别率达到96.78％,平均识别时间0.52 s,明显高于BP、支持向量机(SVM)等方法.