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...     

Credit score classification using spiking extreme learning machine

Credit score classification is a prominent research problem in the banking or financial industry, and its predictive performance is responsible for the profitability of financial industry. This paper addresses how Spiking Extreme Learning Machine (SELM) can be effectively used for credit score classification. A novel spike-generating function is proposed in Leaky Nonlinear Integrate and Fire Model (LNIF). Its interspike period is computed and utilized in the extreme learning machine (ELM) for credit score classification. The proposed model is named as SELM and is validated on five real-world credit scoring datasets namely: Australian, German-categorical, German-numerical, Japanese, and Bankruptcy. Further, results obtained by SELM are compared with back propagation, probabilistic neural network, ELM, voting-based Q-generalized extreme learning machine, Radial basis neural network and ELM with some existing spiking neuron models in terms of classification accuracy, Area under curve (AUC), H-measure and computational time. From the experimental results, it has been noticed that improvement in accuracy and execution time for the proposed SELM is highly statistically important for all aforementioned credit scoring datasets. Thus, integrating a biological spiking function with ELM makes it more efficient for categorization.     

Aerial scene classification via an ensemble extreme learning machine classifier based on discriminative hybrid convolutional neural networks features

Identifying a discriminative feature can effectively improve the classification performance of aerial scene classification. Deep convolutional neural networks (DCNN) have been widely used in aerial scene classification for its learning discriminative feature ability. The DCNN feature can be more discriminative by optimizing the training loss function and using transfer learning methods. To enhance the discriminative power of a DCNN feature, the improved loss functions of pretraining models are combined with a softmax loss function and a centre loss function. To further improve performance, in this article, we propose hybrid DCNN features for aerial scene classification. First, we use DCNN models with joint loss functions and transfer learning from pretrained deep DCNN models. Second, the dense DCNN features are extracted, and the discriminative hybrid features are created using linear connection. Finally, an ensemble extreme learning machine (EELM) classifier is adopted for classification due to its general superiority and low computational cost. Experimental results based on the three public benchmark data sets demonstrate that the hybrid features obtained using the proposed approach and classified by the EELM classifier can result in remarkable performance.     

Multiple-kernel-learning-based extreme learning machine for classification design

The extreme learning machine (ELM) is a new method for using single hidden layer feed-forward networks with a much simpler training method. While conventional kernel-based classifiers are based on a single kernel, in reality, it is often desirable to base classifiers on combinations of multiple kernels. In this paper, we propose the issue of multiple-kernel learning (MKL) for ELM by formulating it as a semi-infinite linear programming. We further extend this idea by integrating with techniques of MKL. The kernel function in this ELM formulation no longer needs to be fixed, but can be automatically learned as a combination of multiple kernels. Two formulations of multiple-kernel classifiers are proposed. The first one is based on a convex combination of the given base kernels, while the second one uses a convex combination of the so-called equivalent kernels. Empirically, the second formulation is particularly competitive. Experiments on a large number of both toy and real-world data sets (including high-magnification sampling rate image data set) show that the resultant classifier is fast and accurate and can also be easily trained by simply changing linear program.     

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。     

Generalized eigenvalue extreme learning machine for classification

Applied Intelligence - Extreme learning machine (ELM) has attracted widespread attention due to its simple, quick and good performance. In this work, in order to deal with cross data quickly and...     

改进极限学习机的移动界面模式半监督分类

针对现有半监督分类方法无法对移动界面模式进行有效分类的问题,提出一种采用改进极限学习机的移动界面模式半监督分类方法。为了提高极限学习机的分类效果,利用改进的粒子群优化算法优化极限学习机的初始参数。根据移动界面模式数据的特点,利用主动学习和模糊[C]均值聚类提取信息丰富的未标记数据进行训练和标记。利用分类器实现对所有数据的分类。实验结果表明,该分类方法能够对移动界面模式数据进行有效和合理的分类。     

Probability based voting extreme learning machine for multiclass XML documents classification

This paper presents a novel solution based on Extreme Learning Machine (ELM) for multiclass XML documents classification. ELM is a generalized Single-hidden Layer Feedforward Network (SLFN) with extremely fast learning capacity. An improved vector model DSVM (Distribution based Structured Vector Model) is proposed to represent XML documents with more structural information and more precise semantic information. The XML documents classifiers are conducted based on PV-ELM (Probablity based Voting ELM) with a postprocessing method ε-RCC (ε - Revoting of Confusing Classes) to refine the voting results. To evaluate the overall performance of this solution, a series of experiments are conducted on two real datasets of news feeds online. The experimental results show that DSVM represents the XML documents more effectively and PV-ELM with ε-RCC achieves a higher accuracy than original ELM algorithm for multiclass classification.     

Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution

Extreme learning machine for single-hidden-layer feedforward neural networks has been extensively applied in imbalanced data learning due to its fast learning capability. Ensemble approach can effectively improve the classification performance by combining several weak learners according to a certain rule. In this paper, a novel ensemble approach on weighted extreme learning machine for imbalanced data classification problem is proposed. The weight of each base learner in the ensemble is optimized by differential evolution algorithm. Experimental results on 12 datasets show that the proposed method could achieve more classification performance compared with the simple vote-based ensemble method and non-ensemble method.

     

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。     

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.     

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.     

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.     

Parallelized extreme learning machine for online data classification

Applied Intelligence - The challenges raised by the massive data are being managed by the community through the advancements of infrastructure and algorithms, and now the processing of fast data is...     

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。     

基于机器学习的音频分类

为施行有效的音频分类以高效率处理日渐复杂的音频信息,研究采用包含多种神经网络在内的5种机器学习模型,实现多种决策下的音频分类以寻找最优模型,基于分类准确度对各模型分类效果进行评估,在使用正则化方法保证模型泛化能力的条件下,通过比较和实验,挖掘并验证出了相对最优的模型——卷积神经网络音频分类模型及对应参数,为现有音频分类模型的进一步优化提供了参考方向。     

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%。