极限学习机的分类器集成模型研究

将极限学习机算法与旋转森林算法相结合,提出了以ELM算法为基分类器并以旋转森林算法为框架的RF-ELM集成学习模型。在8个数据集上进行了3组预测实验,根据实验结果讨论了ELM算法中隐含层神经元个数对预测结果的影响以及单个ELM模型预测结果不稳定的缺陷;将RF-ELM模型与单ELM模型和基于Bagging算法集成的ELM模型相比较,由稳定性和预测精度的两组对比实验的实验结果表明,对ELM的集成学习可以有效地提高ELM模型的性能,且RF-ELM模型较其他两个模型具有更好的稳定性和更高的准确率,验证了RF-ELM是一种有效的ELM集成学习模型。     

基于极限学习机的航空发动机传感器故障诊断

针对当前应用于航空发动机传感器故障诊断中的基于梯度的传统学习算法多存在参数选择困难、容易陷入局部最小化、过拟合等问题,提出了基于极限学习机(ELM)的航空发动机传感器故障诊断方法。算法只需设置隐含层神经元的个数,能够较好地避免上述问题,缩短故障诊断时间、提升诊断精度。通过仿真试验表明:基于ELM算法所建的航空发动机传感器故障诊断模型要比基于BP神经网络算法所建的模型耗时短且精度高。     

Length-Changeable Incremental Extreme Learning Machine

Extreme learning machine (ELM) is a learning algorithm for generalized single-hidden-layer feed-forward networks (SLFNs). In order to obtain a suitable network architecture, Incremental Extreme Learning Machine (I-ELM) is a sort of ELM constructing SLFNs by adding hidden nodes one by one. Although kinds of I-ELM-class algorithms were proposed to improve the convergence rate or to obtain minimal training error, they do not change the construction way of I-ELM or face the over-fitting risk. Making the testing error converge quickly and stably therefore becomes an important issue. In this paper, we proposed a new incremental ELM which is referred to as Length-Changeable Incremental Extreme Learning Machine (LCI-ELM). It allows more than one hidden node to be added to the network and the existing network will be regarded as a whole in output weights tuning. The output weights of newly added hidden nodes are determined using a partial error-minimizing method. We prove that an SLFN constructed using LCI-ELM has approximation capability on a universal compact input set as well as on a finite training set. Experimental results demonstrate that LCI-ELM achieves higher convergence rate as well as lower over-fitting risk than some competitive I-ELM-class 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.     

基于蚁群优化的极限学习机选择性集成学习算法

针对现有极限学习机集成学习算法分类精度低、泛化能力差等缺点,提出了一种基于蚁群优化思想的极限学习机选择性集成学习算法。该算法首先通过随机分配隐层输入权重和偏置的方法生成大量差异的极限学习机分类器,然后利用一个二叉蚁群优化搜索算法迭代地搜寻最优分类器组合,最终使用该组合分类测试样本。通过12个标准数据集对该算法进行了测试,该算法在9个数据集上获得了最优结果,在另3个数据集上获得了次优结果。采用该算法可显著提高分类精度与泛化性能。     

拉普拉斯多层极速学习机

极速学习机不仅仅是有效的分类器,还能应用到半监督学习中.但是,半监督极速学习机和拉普拉斯光滑孪生支持向量机一样是一种浅层学习算法.深度学习实现了复杂函数的逼近并缓解了以前多层神经网络算法的局部最小性问题,目前在机器学习领域中引起了广泛的关注.多层极速学习机（ML-ELM）是根据深度学习和极速学习机的思想提出的算法,通过堆叠极速学习机-自动编码器算法（ELM-AE）构建多层神经网络模型,不仅实现复杂函数的逼近,并且训练过程中无需迭代,学习效率高.我们把流形正则化框架引入ML-ELM中提出拉普拉斯多层极速学习机算法（Lap-ML-ELM）.然而,ELM-AE不能很好的解决过拟合问题,针对这一问题我们把权值不确定引入ELM-AE中提出权值不确定极速学习机-自动编码器算法（WU-ELM-AE）,它学习到更为鲁棒的特征.最后,我们在前面两种算法的基础上提出权值不确定拉普拉斯多层极速学习机算法（WUL-ML-ELM）,它堆叠WU-ELM-AE构建深度模型,并且用流形正则化框架求取输出权值,该算法在分类精度上有明显提高并且不需花费太多的时间.实验结果表明,Lap-ML-ELM与WUL-ML-ELM都是有效的半监督学习算法.     

Multi layer ELM-RBF for multi-label learning

Many neural network methods such as ML-RBF and BP-MLL have been used for multi-label classification. Recently, extreme learning machine (ELM) is used as the basic elements to handle multi-label classification problem because of its fast training time. Extreme learning machine based auto encoder (ELM-AE) is a novel method of neural network which can reproduce the input signal as well as auto encoder, but it can not solve the over-fitting problem in neural networks elegantly. Introducing weight uncertainty into ELM-AE, we can treat the input weights as random variables following Gaussian distribution and propose weight uncertainty ELM-AE (WuELM-AE). In this paper, a neural network named multi layer ELM-RBF for multi-label learning (ML-ELM-RBF) is proposed. It is derived from radial basis function for multi-label learning (ML-RBF) and WuELM-AE. ML-ELM-RBF firstly stacks WuELM-AE to create a deep network, and then it conducts clustering analysis on samples features of each possible class to compose the last hidden layer. ML-ELM-RBF has achieved satisfactory results on single-label and multi-label data sets. Experimental results show that WuELM-AE and ML-ELM-RBF are effective learning algorithms.     

集成重复训练极限学习机的数据分类

极限学习机是一种随机化算法,它随机生成单隐含层神经网络输入层连接权和隐含层偏置,用分析的方法确定输出层连接权。给定网络结构,用极限学习机重复训练网络,会得到不同的学习模型。本文提出了一种集成模型对数据进行分类的方法。首先用极限学习机算法重复训练若干个单隐含层前馈神经网络,然后用多数投票法集成训练好的神经网络,最后用集成模型对数据进行分类,并在10个数据集上和极限学习机及集成极限学习机进行了实验比较。实验结果表明,本文提出的方法优于极限学习机和集成极限学习机。     

Model selection of extreme learning machine based on multi-objective optimization

As a novel learning algorithm for single-hidden-layer feedforward neural networks, extreme learning machines (ELMs) have been a promising tool for regression and classification applications. However, it is not trivial for ELMs to find the proper number of hidden neurons due to the nonoptimal input weights and hidden biases. In this paper, a new model selection method of ELM based on multi-objective optimization is proposed to obtain compact networks with good generalization ability. First, a new leave-one-out (LOO) error bound of ELM is derived, and it can be calculated with negligible computational cost once the ELM training is finished. Furthermore, the hidden nodes are added to the network one-by-one, and at each step, a multi-objective optimization algorithm is used to select optimal input weights by minimizing this LOO bound and the norm of output weight simultaneously in order to avoid over-fitting. Experiments on five UCI regression data sets are conducted, demonstrating that the proposed algorithm can generally obtain better generalization performance with more compact network than the conventional gradient-based back-propagation method, original ELM and evolutionary ELM.     

一种基于互信息变量选择的极端学习机算法

针对回归问题中存在的变量选择和网络结构设计问题, 提出一种基于互信息的极端学习机(ELM) 训练算法, 同时实现输入变量的选择和隐含层的结构优化. 该算法将互信息输入变量选择嵌入到ELM网络的学习过程之中, 以网络的学习性能作为衡量输入变量与输出变量相关与否的指标, 并以增量式的方法确定隐含层节点的规模.在Lorenz、Gas Furnace 和10 组标杆数据上的仿真结果表明了所提出算法的有效性. 该算法不仅可以简化网络结构, 还可以提高网络的泛化性能.

     

极限学习机在机场旅客吞吐量预测中的应用

极限学习机(ELM)是一种新型单馈层神经网络算法,在训练过程中只需要设置合适的隐藏层节点个数,随机赋值输入权值和隐藏层偏差,一次完成无需迭代.结合遗传算法在预测模型参数寻优方面的优势,找到极限学习机的最优参数取值,建立成都双流国际机场旅客吞吐量预测模型,通过对比支持向量机、BP神经网络,分析遗传-极限学习机算法在旅客吞吐量预测中的可行性和优势.仿真结果表明遗传-极限学习机算法不仅可行,并且与原始极限学习机算法相比,在预测精度和训练速度上具有比较明显的优势.     

基于近似结构风险的ELM隐层节点数优化

隐层节点数是影响极端学习机(ELM)泛化性能的关键参数,针对传统的ELM隐层节点数确定算法中优化过程复杂、容易过学习或陷入局部最优的问题,提出结构风险最小化-极端学习机(SRM-ELM)算法。通过分析VC维与隐层节点数量之间的关联,对VC信任函数进行近似改进,使其为凹函数,并结合经验风险重构近似的SRM。在此基础上,将粒子群优化的位置值直接作为ELM的隐层节点数,利用粒子群算法最小化结构风险函数获得极端学习机的隐层节点数,作为最优节点数。使用6组UCI数据和胶囊缺陷数据进行仿真验证,结果表明,该算法能获得极端学习机的最优节点数,并具有更好的泛化能力。     

改进人工蜂群优化的极限学习机

针对极限学习机(ELM)在训练过程中需要大量隐含层节点的问题,提出了差分进化与克隆算法改进人工蜂群优化的极限学习机(DECABC-ELM),在人工蜂群算法的基础上,引入了差分进化算法的差分变异算子和免疫克隆算法的克隆扩增算子,改进了人工蜂群收敛速度慢等缺点,使用改进的人工蜂群算法计算ELM的隐含层节点参数.将算法应用于回归和分类数据集,并与其他算法进行比较,获得了良好的效果.     

改进鱼群算法优化的ELM在乳腺肿瘤辅助诊断中的应用研究

针对传统极限学习机的输入权值矩阵和隐含层偏差是随机给定进而可能会导致在乳腺肿瘤的辅助诊断应用研究中存在精度明显不足的情况,提出用改进鱼群算法优化ELM方法。在完成对乳腺肿瘤有效的辅助诊断的过程中,本研究工作充分利用ELM能快速地完成训练过程且具有很好的泛化能力的特点,并结合用改进鱼群算法对ELM的隐含层偏差进行优化,构造出了乳腺肿瘤与从乳腺肿瘤样本数据中提取的10个特征向量之间的非线性映射关系。将本文提出的乳腺肿瘤识别方法的仿真结果与AFSA-ELM方法、ELM方法、LVQ方法、BP方法的仿真结果分别从识别准确率、假阴性率、学习速度三个方面做对比分析,仿真结果表明,本文所提方法对乳腺肿瘤诊断具有较高的分类识别准确率、假阴性率以及较快的学习速率。     

基于带惩罚因子椭球定界算法的软测量建模

软测量模型的预测精度和泛化性能是软测量建模的2个重要指标。基于最优定界椭球的极限学习机算法(OBE-ELM)虽然克服了传统极限学习机建模预测精度不高、预测结果不稳定等缺点,但是传统OBE算法仅考虑模型误差最小化,未考虑模型的复杂程度,导致模型易出现过拟合现象。基于上述问题,首先针对噪声未知但有界的非线性系统,提出了一种带惩罚项的椭球定界算法(POBE),在模型误差中加入惩罚项起到抑制参数增长太大和驱使不重要参数逐渐减小到零的作用,然后将POBE应用到ELM模型参数优化过程中。最后在信道参数估计实验和连续搅拌反应釜数据集上分别验证POBE及POBE-ELM有效性。     

基于影响度剪枝的极端学习机分类算法研究

针对极端学习机(ELM)网络规模控制问题,从剪枝思路出发,提出了一种基于影响度剪枝的ELM分类算法。利用ELM网络单个隐节点连接输入层和输出层的权值向量、该隐节点的输出、初始隐节点个数以及训练样本个数,定义单个隐节点相对于整个网络学习的影响度,根据影响度判断隐节点的重要性并将其排序,采用与ELM网络规模相匹配的剪枝步长删除冗余节点,最后更新隐含层与输入层和输出层连接的权值向量。通过对多个UCI机器学习数据集进行分类实验,并将提出的算法与EM-ELM、PELM和ELM算法相比较,结果表明,该算法具有较高的稳定性和测试精度,训练速度较快,并能有效地控制网络规模。     

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.     

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.     

Extreme learning machine for interval neural networks

Interval data offer a valuable way of representing the available information in complex problems where uncertainty, inaccuracy, or variability must be taken into account. Considered in this paper is the learning of interval neural networks, of which the input and output are vectors with interval components, and the weights are real numbers. The back-propagation (BP) learning algorithm is very slow for interval neural networks, just as for usual real-valued neural networks. Extreme learning machine (ELM) has faster learning speed than the BP algorithm. In this paper, ELM is applied for learning of interval neural networks, resulting in an interval extreme learning machine (IELM). There are two steps in the ELM for usual feedforward neural networks. The first step is to randomly generate the weights connecting the input and the hidden layers, and the second step is to use the Moore–Penrose generalized inversely to determine the weights connecting the hidden and output layers. The first step can be directly applied for interval neural networks. But the second step cannot, due to the involvement of nonlinear constraint conditions for IELM. Instead, we use the same idea as that of the BP algorithm to form a nonlinear optimization problem to determine the weights connecting the hidden and output layers of IELM. Numerical experiments show that IELM is much faster than the usual BP algorithm. And the generalization performance of IELM is much better than that of BP, while the training error of IELM is a little bit worse than that of BP, implying that there might be an over-fitting for BP.     

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。