多层极限学习机在入侵检测中的应用

针对神经网络在入侵检测应用存在的维度高、数据大、获取标记样本难、特征构造难、训练难等问题,提出了一种基于深度多层极限学习机(ML-ELM)的入侵检测方法。首先,采用多层网络结构和深度学习方法抽取检测样本最高层次的抽象特征,用奇异值对入侵检测数据进行特征表达;然后,利用极限学习机(ELM)建立入侵检测数据的分类模型;其次,利用逐层的无监督学习方法解决入侵检测获取标记样本难的问题;最后采用KDD99数据集对该方法的性能进行了验证。实验结果表明:多层极限学习机的方法提高了检测正确率,检测漏报率也低至0.48%,检测速度比其他深度模型的检测方法提高了6倍以上。同时在极少标记样本的情况下仍有85%以上的正确率。通过多层网络结构的构建提高了对U2L、R2L这两类攻击的检测率。该方法集成深度学习和无监督学习的优点,能对高维度,大数据的网络记录用较少的参数得到更好的表达,在入侵检测的检测速度以及特征表达两个方面都具有优势。     

Fingerprint matching based on extreme learning machine

Considering fingerprint matching as a classification problem, the extreme learning machine (ELM) is a powerful classifier for assigning inputs to their corresponding classes, which offers better generalization performance, much faster learning speed, and minimal human intervention, and is therefore able to overcome the disadvantages of other gradient-based, standard optimization-based, and least squares-based learning techniques, such as high computational complexity, difficult parameter tuning, and so on. This paper proposes a novel fingerprint recognition system by first applying the ELM and Regularized ELM (R-ELM) to fingerprint matching to overcome the demerits of traditional learning methods. The proposed method includes the following steps: effective preprocessing, extraction of invariant moment features, and PCA for feature selection. Finally, ELM and R-ELM are used for fingerprint matching. Experimental results show that the proposed methods have a higher matching accuracy and are less time-consuming; thus, they are suitable for real-time processing. Other comparative studies involving traditional methods also show that the proposed methods with ELM and R-ELM outperform the traditional ones.     

Surface reconstruction based on extreme learning machine

In this paper, extreme learning machine (ELM) is used to reconstruct a surface with a high speed. It is shown that an improved ELM, called polyharmonic extreme learning machine (P-ELM), is proposed to reconstruct a smoother surface with a high accuracy and robust stability. The proposed P-ELM improves ELM in the sense of adding a polynomial in the single-hidden-layer feedforward networks to approximate the unknown function of the surface. The proposed P-ELM can not only retain the advantages of ELM with an extremely high learning speed and a good generalization performance but also reflect the intrinsic properties of the reconstructed surface. The detailed comparisons of the P-ELM, RBF algorithm, and ELM are carried out in the simulation to show the good performances and the effectiveness of the proposed algorithm.     

基于最大相关和最小冗余准则及极限学习机的癫痫发作检测方法

癫痫发作检测可以实现脑电分类和病灶定位,对癫痫的临床治疗具有重要意义。针对大数据量、高特征值空间长程脑电的快速和准确分类问题,提出一种基于最大相关和最小冗余准则及极限学习机的癫痫发作检测方法。对脑电信号进行短时傅里叶变换,并选取能量时频分布为特征,利用基于最大相关和最小冗余准则的方法进行特征选择,并使用极限学习机、支持向量机和反向传播算法对癫痫不同状态进行分类和判别。实验结果表明,极限学习机的分类准确率和训练速度两方面性能优于支持向量机和反向传播算法,发作间期和发作期的分类准确率达到98%以上,训练时间仅为0.8s,所提方法能够实时准确地检测癫痫发作。     

Face recognition based on extreme learning machine

Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machine (SVM) are equivalent but ELM has less stringent optimization constraints. Due to the mild optimization constraints ELM can be easy of implementation and usually obtains better generalization performance. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications. The performance is verified through four benchmarking face image data sets.     

基于极限学习机与改进K-means算法的入侵检测方法

入侵检测系统对于保障网络安全至关重要。针对传统的单一检测算法很难对不同种类的攻击都有很好检测效果的问题,提出一种结合极限学习机与改进K means算法的入侵检测方法。基于算法级联的方式,利用新型线性修正单元（PReLU）激活函数对极限学习机算法进行优化,采用设置距离阈值的方式,实现K means算法自动选择初始聚类中心与聚类簇数目的双重优化,设计了一种混合式入侵检测方法。采用NSL KDD数据集对所提出的入侵检测方法进行仿真实验,实验结果表明,与传统的BP神经网络、支持向量机与极限学习机算法相比,该方法有效地提高了检测效果,同时降低了误报率。     

Brain tumor classification using modified kernel based softplus extreme learning machine

Multimedia Tools and Applications - An uncontrollable growth of abnormal cells in the brain may result in brain tumor. Two different categories of brain tumor are benign and malignant. The doctors...     

基于核极限学习机的标记分布学习

标记分布学习作为一种新的学习范式,利用最大熵模型构造的专用化算法能够很好地解决某些标记多样性问题,但是计算量巨大。基于此,引入运行速度快、稳定性更高的核极限学习机模型,提出基于核极限学习机的标记分布学习算法（KELM-LDL）。首先在极限学习机算法中通过RBF核函数将特征映射到高维空间,然后对原标记空间建立KELM回归模型求得输出权值,最后通过模型计算预测未知样本的标记分布。与现有算法在各领域不同规模数据集的实验表明,实验结果均优于多个对比算法,统计假设检验进一步说明KELM-LDL算法的有效性和稳定性。     

基于去噪自编码器的极限学习机

针对极限学习机算法（ELM）参数随机赋值降低算法鲁棒性及性能受噪声影响显著的问题，将去噪自编码器（DAE）与ELM算法相结合，提出了基于去噪自编码器的极限学习机算法（DAE-ELM）。首先，通过去噪自编码器产生ELM的输入数据、输入权值与隐含层参数；然后，以ELM求得隐含层输出权值，完成对分类器的训练。该算法一方面继承了DAE的优点，自动提取的特征更具代表性与鲁棒性，对于噪声有较强的抑制作用；另一方面克服了ELM参数赋值的随机性，增强了算法鲁棒性。实验结果表明，在不含噪声影响下DAE-ELM相较于ELM、PCA-ELM、SAA-2算法，其分类错误率在MNIST数据集中至少下降了5.6%，在Fashion MNIST数据集中至少下降了3.0%，在Rectangles数据集中至少下降了2.0%，在Convex数据集中至少下降了12.7%。     

Text categorization based on regularization extreme learning machine

This article proposes a novel approach for text categorization based on a regularization extreme learning machine (RELM) in which its weights can be obtained analytically, and a bias-variance trade-off could be achieved by adding a regularization term into the linear system of single-hidden layer feedforward neural networks. To fit the input scale of RELM, the latent semantic analysis was used to represent text for dimensionality reduction. Moreover, a classification algorithm based on RELM was developed including the uni-label (i.e., a document can only be assigned to a unique category) and multi-label (i.e., a document can be assigned to multiple categories simultaneously) situations. The experimental results in two benchmarks show that the proposed method can produce good performance in most cases, and it could learn faster than popular methods such as feedforward neural networks or support vector machine.     

基于改进极限学习机的软测量建模

极限学习机（ELM）因其泛化能力好和学习速度快而成为软测量的新方法,但当应用到铝电解工艺参数建模时,ELM通常需要较多隐层节点并且泛化能力较低。针对这一问题,提出一种基于改进极限学习机（IELM）的软测量模型。首先,利用粗糙集中的约简理论剔除输入变量中的冗余或不相关属性,以降低ELM的输入复杂性;然后,利用偏相关系数对输入变量和输出变量间的相关性进行分析,将输入数据分为正输入和负输入两部分,分别对这两部分建立输入单元,重新构建ELM网络;最后,建立了基于改进极限学习机的铝电解分子比软测量模型。仿真实验结果表明,基于改进极限学习机的软测量模型具有较好的泛化能力和稳定性。     

基于有效K均值有效极限学习机的混沌海杂波背景中微弱信号检测

为了在复杂混沌噪声背景中快速准确提取有用信号,提出基于复杂非线性系统相空间重构理论,采用改进极限学习机(ELM)预测单步误差检测微弱信号的方法。采用改进K均值聚类算法选择最优族作训练集,改进极限学习机选择权值和偏置的方法进一步提高检测的精度和速度,采用Lorenz系统建立了混沌噪声序列的一步预测模型,从预测误差中检测湮没在混沌噪声中的微弱目标信号(包括周期信号和瞬态信号),然后使用加拿大Mc Master大学IPIX雷达数据,在海杂波噪声中提取漂浮物信号作为实验研究。结果表明该方法能够有效检测混沌背景噪声中极微弱信号,同时抑制噪声对混沌背景信号的影响,与径向基函数(RBF)神经网络等传统算法相比,预测精度提升了25%,检测门限提高了-5 dB,同时训练用时减少77.1 s,在实际应用中具有更明显优势。     

改进的灵敏度剪枝极限学习机

针对极限学习机(ELM) 网络结构优化问题, 提出一种改进的灵敏度剪枝ELM(ImSAP-ELM). ImSAP-ELM 将??₂ 正则化因子引入SAP-ELM 中, 采用留一准则确定最优隐节点数. 推导基于奇异值分解的输出权重计算公式, 避免矩阵奇异导致求解无效的问题. 将ImSAP-ELM 用于故障预测, 利用多组同类型故障数据建立多个ImSAP-ELM 模型, 基于加权思想融合不同ImSAP-ELM 的预测值. 某型无人机发射机实例表明, 相比于ELM、OP-ELM (最优剪枝ELM) 和SAP-ELM, ImSAP-ELM 耗时最高, 但是ImSAP-ELM 的预测误差小于其他3 种方法.

     

Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system

Intrusion detection has become essential to network security because of the increasing connectivity between computers. Several intrusion detection systems have been developed to protect networks using different statistical methods and machine learning techniques. This study aims to design a model that deals with real intrusion detection problems in data analysis and classify network data into normal and abnormal behaviors. This study proposes a multi-level hybrid intrusion detection model that uses support vector machine and extreme learning machine to improve the efficiency of detecting known and unknown attacks. A modified K-means algorithm is also proposed to build a high-quality training dataset that contributes significantly to improving the performance of classifiers. The modified K-means is used to build new small training datasets representing the entire original training dataset, significantly reduce the training time of classifiers, and improve the performance of intrusion detection system. The popular KDD Cup 1999 dataset is used to evaluate the proposed model. Compared with other methods based on the same dataset, the proposed model shows high efficiency in attack detection, and its accuracy (95.75%) is the best performance thus far.     

一种利用极限学习机的数据可视化方法

提出一种利用极限学习机ELM的数据可视化方法,该方法利用多维尺度分析MDS、Pearson相关性、Spearman相关性代替常用的均方误差MSE实现高维数据投影到2-维平面的数据可视化。将所提方法与近期流行的随机邻域嵌入SNE及其改进的t-SNE方法对比,并通过局部连续元准则LCMC进行质量评测。结果表明:该方法的数据可视化结果及计算性能明显优于SNE及t-SNE方法;而在提出的三种学习规则中,基于MDS的学习规则效果最好。     

一种鲁棒非平衡极速学习机算法

极速学习机（ELM）算法只对平衡数据集分类较好,对于非平衡数据集,它通常偏向多数样本类,对于少数样本类性能较低。针对这一问题,提出了一种处理不平衡数据集分类的ELM模型（ELM-CIL）,该模型按照代价敏感学习的原则为少数类样本赋予较大的惩罚系数,并引入模糊隶属度值减小了外围噪声点的影响。实验表明,提出的方法不仅对提高不平衡数据集中少数类的分类精度效果较明显,而且提高了对噪声的鲁棒性。     

基于改进极端学习机的网络流量预测

为了提高网络流量的预测精度,针对极端学习机的训练样本选择问题,提出一种改进极端学习机的网络流量预测模型（IELM）。根据最优延迟时间和嵌入维数对网络流量重构,建立网络学习样本,将学习样本输入到改进极端学习机进行训练,随新样本加入而逐步求解网络的权值,以提高学习速度,引入cholesky分解方法提高模型的泛化能力,采用具体网络流量数据进行了仿真测试。结果表明,IELM不仅可以获得较传统网络流量预测模型更高的精度,并且大幅度减少了计算时间,提高了建模效率,可以较好地满足网络流量预测要求。     

基于极限学习机的网页分类应用

极限学习机ELM不同于传统的神经网络学习算法（如BP算法）,是一种高效的单隐层前馈神经网络（SLFNs）学习算法。将极限学习机引入到中文网页分类任务中。对中文网页进行预处理,提取其特性信息,从而形成网页特征树,产生定长编码作为极限学习机的输入数据。实验结果表明该方法能够有效地分类网页。     

Dynamic ensemble extreme learning machine based on sample entropy

Extreme learning machine (ELM) as a new learning algorithm has been proposed for single-hidden layer feed-forward neural networks, ELM can overcome many drawbacks in the traditional gradient-based learning algorithm such as local minimal, improper learning rate, and low learning speed by randomly selecting input weights and hidden layer bias. However, ELM suffers from instability and over-fitting, especially on large datasets. In this paper, a dynamic ensemble extreme learning machine based on sample entropy is proposed, which can alleviate to some extent the problems of instability and over-fitting, and increase the prediction accuracy. The experimental results show that the proposed approach is robust and efficient.     

基于层次分析法与差分进化算法极限学习机的自动扶梯故障检测

针对自动扶梯故障问题,以层次分析和差分进化算法极限学习机相结合的方式快速、准确地分析了自动扶梯发生的故障问题。首先,用层次分析法计算出各故障因数的权值,选取权值较大的一部分因素作为输入。然后,建立DE-ELM安全评测模型并与ELM模型比较,得出自动扶梯安全程度等级并说明自动扶梯的安全性。研究表明:对于很难或无法获得故障因素准确值的自动扶梯而言,层次分析法是一种有效实用的可靠性分析方法,再结合差分进化算法极限学习机的全局寻优能力,对自动扶梯故障问题的检测更加快速、准确。