#br# 耦合样本先验分布信息的加权极限学习机

极限学习机广泛用于分类、聚类、回归等任务中，但在处理类不平衡分类问题时，前人未充分考虑样本先验分布信息对分类性能的影响。针对此问题，本文提出耦合样本先验分布信息的加权极限学习机（Coupling sample Prior distribution Weighted Extreme Learning Machine，CPWELM）算法。该算法基于加权极限学习机，充分探讨不同分布样本点的重要程度，以此构造代价矩阵，进而提升分类器性能。本文通过12个不平衡数据集，对CPWELM算法的可行性及有效性进行了验证。结果表明，相比同类其他算法，CPWELM算法的性能更优。     

类不平衡模糊加权极限学习机算法研究

从理论上分析了样例不平衡分布对极限学习机性能产生危害的原因;在该理论框架下探讨了加权极限学习机在处理此类问题上的有效性及其固有缺陷;引入模糊集的思想,对传统的加权极限学习机进行了改进,并提出了4种用于解决类不平衡问题的模糊加权极限学习机算法;最后通过20个基准的二类不平衡数据集对所提算法的有效性和可行性进行了验证。实验结果表明:较之加权极限学习机及几种传统的不平衡极限学习机算法,提出的算法可明显获得更优的分类性能,并且与模糊加权支持向量机系列算法相比,所提算法通常可获得与之相当的分类性能,但时间开销往往更小。     

面向失衡数据的自适应加权ELM分类算法

为提高极限学习机在失衡数据中的整体分类性能,提出一种基于代价敏感学习的自适应加权极限学习机分类算法。考虑各类间样本的差异性和同一类内样本的丰富性,利用类样本数量差异构造初始惩罚权重,分析样本附近异类样本数量确定额外代价权重,将两种代价权重相加构建自适应代价敏感惩罚矩阵。在公共数据集上的一系列对比实验结果表明,采用的自适应加权策略兼顾了不同类别样本的分布,在不平衡数据集上有效提高了算法整体分类精度。     

基于加权极限学习机的异常轨迹检测算法

针对现有异常轨迹检测中分类不平衡造成难以确定最优分类面的问题,提出一种基于加权极限学习机（ELM,Extreme Learning Machine）的异常轨迹检测算法。该算法采用加权ELM克服轨迹数据不平衡造成的分类面偏移,通过对正、负两类样本合理分配权重,并构造最优分类面获得较好的异常检测效果。仿真实验表明,加权ELM算法在训练速度,准确率,整体性能等方面均优干传统SVM和BP网络分类方法。     

应用于人脸图像识别的邻域保持极限学习机

极限学习机广泛应用于人脸识别领域。传统的极限学习机算法因在少量标签样本上进行训练，容易发生学习过程不充分问题，同时在学习过程中往往忽略了样本内在的几何结构，影响其对人脸识别的分类能力。受流形学习思想的启发，提出一种邻域保持极限学习机算法。该算法保持数据最本质的结构和同类数据的判别信息，利用最小化类内散度矩阵来提高极限学习机整体的分类性能。通过人脸数据集上的多次实验结果表明，该算法的人脸识别准确率高于其他算法，更能有效地进行分类识别。     

基于聚类欠采样的极端学习机

针对极端学习机算法对不平衡数据分类问题的处理效果不够理想,提出了一种基于聚类欠采样的极端学习机算法。新算法首先对训练集的负类样本进行聚类生成不同的簇,然后在各簇中按规定的采样率对其进行欠采样,取出的样本组成新的负类数据集,从而使训练集正负类数据个数达到相对平衡,最后训练分类器对测试集进行测试。实验结果表明,新算法有效地降低了数据的不平衡对分类准确率的影响,具有更好的分类性能。     

基于不均衡样本重构的加权在线贯序极限学习机

针对现有学习算法难以有效提高不均衡在线贯序数据中少类样本分类精度的问题,提出一种基于不均衡样本重构的加权在线贯序极限学习机。该算法从提取在线贯序数据的分布特性入手,主要包括离线和在线两个阶段:离线阶段主要采用主曲线构建少类样本的可信区域,并通过对该区域内样本进行过采样,来构建符合样本分布趋势的均衡样本集,进而建立初始模型;而在线阶段则对贯序到达的数据根据训练误差赋予各样本相应权重,同时动态更新网络权值。采用UCI标准数据集和澳门实测气象数据进行实验对比,结果表明,与现有在线贯序极限学习机(OS-ELM)、极限学习机(ELM)和元认知在线贯序极限学习机(MCOS-ELM)相比,所提算法对少类样本的识别能力更高,且所提算法的模型训练时间与其他三种算法相差不大。结果表明在不影响算法复杂度的情况下,所提算法能有效提高少类样本的分类精度。     

面对类别不平衡的增量在线序列极限学习机

针对在线序列极限学习机对于类别不平衡数据的学习效率低、分类准确率差的问题,提出了面对类别不平衡的增量在线序列极限学习机(IOS-ELM)。该算法根据类别不平衡比例调整平衡因子,利用分块矩阵的广义逆矩阵对隐含层节点数进行寻优,提高了模型对类别不平衡数据的在线处理能力,最后通过14个二类和多类不平衡数据集对该算法有效性和可行性进行验证。实验结果表明:该算法与同类其他算法相比具有更好的泛化性和准确率,适用于类别不平衡场景下的在线学习。     

基于深度序列加权核极限学习的入侵检测算法

针对海量多源异构且数据分布不平衡的网络入侵检测问题以及传统深度学习算法无法根据实时入侵情况在线更新其输出权重的问题,提出了一种基于深度序列加权核极限学习的入侵检测算法（DBN-WOS-KELM算法）。该算法先使用深度信念网络DBN对历史数据进行学习,完成对原始数据的特征提取和数据降维,再利用加权序列核极限学习机进行监督学习完成入侵识别,结合了深度信念网络提取抽象特征的能力以及核极限学习机的快速学习能力。最后在部分KDD99数据集上进行了仿真实验,实验结果表明DBN-WOS-KELM算法提高了对小样本攻击的识别率,并且能够根据实际情况在线更新输出权重,训练效率更高。     

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。     

基于流形学习的极端学习机及其在人脸识别中的应用

极端学习机以其快速高效和良好的泛化能力在模式识别领域得到了广泛应用,然而现有的ELM及其改进算法并没有充分考虑到数据维数对ELM分类性能和泛化能力的影响,当数据维数过高时包含的冗余属性及噪音点势必降低ELM的泛化能力,针对这一问题本文提出一种基于流形学习的极端学习机,该算法结合维数约减技术有效消除数据冗余属性及噪声对ELM分类性能的影响,为验证所提方法的有效性,实验使用普遍应用的图像数据,实验结果表明本文所提算法能够显著提高ELM的泛化性能。     

局部感知的类限制极限学习机

极限学习机(ELM)由于高效的训练方式被广泛应用于分类回归,然而不同的输入权值在很大程度上会影响其学习性能。为了进一步提高ELM的学习性能,针对ELM的输入权值进行了研究,充分利用图像局部感知的稀疏性,将局部感知的方法运用到基于自动编码器的ELM(ELM-AE)上,提出了局部感知的类限制极限学习机(RF-C₂ELM)。通过对MNIST数据集进行分类问题分析实验,实验结果表明,在具有相同隐层结点数的条件下,提出的方法能够获得更高的分类精度。     

Extreme learning machine for structured output spaces

Recently, extreme learning machine (ELM) has attracted increasing attention due to its successful applications in classification, regression, and ranking. Normally, the desired output of the learning system using these machine learning techniques is a simple scalar output. However, there are many applications in machine learning which require more complex output rather than a simple scalar one. Therefore, structured output is used for such applications where the system is trained to predict structured output instead of simple one. Previously, support vector machine (SVM) has been introduced for structured output learning in various applications. However, from machine learning point of view, ELM is known to offer better generalization performance compared to other learning techniques. In this study, we extend ELM to more generalized framework to handle complex outputs where simple outputs are considered as special cases of it. Besides the good generalization property of ELM, the resulting model will possesses rich internal structure that reflects task-specific relations and constraints. The experimental results show that structured ELM achieves similar (for binary problems) or better (for multi-class problems) generalization performance when compared to ELM. Moreover, as verified by the simulation results, structured ELM has comparable or better precision performance with structured SVM when tested for more complex output such as object localization problem on PASCAL VOC2006. Also, the investigation on parameter selections is presented and discussed for all problems.

     

A wavelet extreme learning machine

Extreme learning machine (ELM) has been widely used in various fields to overcome the problem of low training speed of the conventional neural network. Kernel extreme learning machine (KELM) introduces the kernel method to ELM model, which is applicable in Stat ML. However, if the number of samples in Stat ML is too small, perhaps the unbalanced samples cannot reflect the statistical characteristics of the input data, so that the learning ability of Stat ML will be influenced. At the same time, the mix kernel functions used in KELM are conventional functions. Therefore, the selection of kernel function can still be optimized. Based on the problems above, we introduce the weighted method to KELM to deal with the unbalanced samples. Wavelet kernel functions have been widely used in support vector machine and obtain a good classification performance. Therefore, to realize a combination of wavelet analysis and KELM, we introduce wavelet kernel functions to KELM model, which has a mix kernel function of wavelet kernel and sigmoid kernel, and introduce the weighted method to KELM model to balance the sample distribution, and then we propose the weighted wavelet–mix kernel extreme learning machine. The experimental results show that this method can effectively improve the classification ability with better generalization. At the same time, the wavelet kernel functions perform very well compared with the conventional kernel functions in KELM model.     

The extreme learning machine learning algorithm with tunable activation function

In this paper, we propose an extreme learning machine (ELM) with tunable activation function (TAF-ELM) learning algorithm, which determines its activation functions dynamically by means of the differential evolution algorithm based on the input data. The main objective is to overcome the problem dependence of fixed slop of the activation function in ELM. We mainly considered the issue of processing of benchmark problems on function approximation and pattern classification. Compared with ELM and E-ELM learning algorithms with the same network size or compact network configuration, the proposed algorithm has improved generalization performance with good accuracy. In addition, the proposed algorithm also has very good performance in the TAF neural networks learning algorithms.     

Manifold regularized extreme learning machine

Extreme learning machine (ELM) works for generalized single-hidden-layer feedforward networks (SLFNs), and its essence is that the hidden layer of SLFNs need not be tuned. But ELM only utilizes labeled data to carry out the supervised learning task. In order to exploit unlabeled data in the ELM model, we first extend the manifold regularization (MR) framework and then demonstrate the relation between the extended MR framework and ELM. Finally, a manifold regularized extreme learning machine is derived from the proposed framework, which maintains the properties of ELM and can be applicable to large-scale learning problems. Experimental results show that the proposed semi-supervised extreme learning machine is the most cost-efficient method. It tends to have better scalability and achieve satisfactory generalization performance at a relatively faster learning speed than traditional semi-supervised learning algorithms.     

Self-adaptive extreme learning machine

In order to overcome the disadvantage of the traditional algorithm for SLFN (single-hidden layer feedforward neural network), an improved algorithm for SLFN, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem. In this paper, a self-adaptive mechanism is introduced into the ELM. Herein, a new variant of ELM, called self-adaptive extreme learning machine (SaELM), is proposed. SaELM is a self-adaptive learning algorithm that can always select the best neuron number in hidden layer to form the neural networks. There is no need to adjust any parameters in the training process. In order to prove the performance of the SaELM, it is used to solve the Italian wine and iris classification problems. Through the comparisons between SaELM and the traditional back propagation, basic ELM and general regression neural network, the results have proven that SaELM has a faster learning speed and better generalization performance when solving the classification problem.     

Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.     

Decay-weighted extreme learning machine for balance and optimization learning

The original extreme learning machine (ELM) was designed for the balanced data, and it balanced misclassification cost of every sample to get the solution. Weighted extreme learning machine assumed that the balance can be achieved through the equality of misclassification costs. This paper improves previous weighted ELM with decay-weight matrix setting for balance and optimization learning. The decay-weight matrix is based on the sample number of each class, but the weight sum values of each class are not necessarily equal. When the number of samples is reduced, the weight sum is also reduced. By adjusting the decaying velocity, classifier could achieve more appropriate boundary position. From the experimental results, the decay-weighted ELM obtains the better effects in solving the imbalance classification tasks, particularly in multiclass tasks. This method was successfully applied to build the prediction model in the urban traffic congestion prediction system.