A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment

To achieve high assessment accuracy for credit risk, a novel multistage deep belief network (DBN) based extreme learning machine (ELM) ensemble learning methodology is proposed. In the proposed methodology, three main stages, i.e., training subsets generation, individual classifiers training and final ensemble output, are involved. In the first stage, bagging sampling algorithm is applied to generate different training subsets for guaranteeing enough training data. Second, the ELM, an effective AI forecasting tool with the unique merits of time-saving and high accuracy, is utilized as the individual classifier, and diverse ensemble members can be accordingly formulated with different subsets and different initial conditions. In the final stage, the individual results are fused into final classification output via the DBN model with sufficient hidden layers, which can effectively capture the valuable information hidden in ensemble members. For illustration and verification, the experimental study on one publicly available credit risk dataset is conducted, and the results show the superiority of the proposed multistage DBN-based ELM ensemble learning paradigm in terms of high classification accuracy.     

Simulating Discrete and Rhythmic Multi-joint Human Arm Movements by Optimization of Nonlinear Performance Indices

An optimization approach applied to mechanical linkage models is used to simulate human arm movements. Predicted arm trajectories are the result of minimizing a nonlinear performance index that depends on kinematic or dynamic variables of the movement. A robust optimization algorithm is presented that computes trajectories which satisfy the necessary conditions with high accuracy. It is especially adapted to the analysis of discrete and rhythmic movements. The optimization problem is solved by parameterizing each generalized coordinate (e.g., joint angular displacement) in terms of Jacobi polynomials and Fourier series, depending on whether discrete or rhythmic movements are considered, combined with a multiple shooting algorithm. The parameterization of coordinates has two advantages. First, it provides an initial guess for the multiple shooting algorithm which solves the optimization problem with high accuracy. Second, it leads to a low dimensional representation of discrete and rhythmic movements in terms of expansion coefficients. The selection of a suitable feature space is an important prerequisite for comparison, recognition and classification of movements. In addition, the separate computational analysis of discrete and rhythmic movements is motivated by their distinct neurophysiological realizations in the cortex. By investigating different performance indices subject to different boundary conditions, the approach can be used to examine possible strategies that humans adopt in selecting specific arm motions for the performance of different tasks in a plane and in three-dimensional space.     

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system — the southern power system of Hebei province.     

Classification of mental tasks from EEG signals using extreme learning machine

In this paper, a recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) is used to classify five mental tasks from different subjects using electroencephalogram (EEG) signals available from a well-known database. Performance of ELM is compared in terms of training time and classification accuracy with a Backpropagation Neural Network (BPNN) classifier and also Support Vector Machines (SVMs). For SVMs, the comparisons have been made for both 1-against-1 and 1-against-all methods. Results show that ELM needs an order of magnitude less training time compared with SVMs and two orders of magnitude less compared with BPNN. The classification accuracy of ELM is similar to that of SVMs and BPNN. The study showed that smoothing of the classifiers' outputs can significantly improve their classification accuracies.     

Fast,Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm

Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the ‘Extreme Learning Machine’ (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random ‘receptive field’ sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.     

A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training

Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.     

Extreme learning machines for regression based on V-matrix method

This paper studies the joint effect of V-matrix, a recently proposed framework for statistical inferences, and extreme learning machine (ELM) on regression problems. First of all, a novel algorithm is proposed to efficiently evaluate the V-matrix. Secondly, a novel weighted ELM algorithm called V-ELM is proposed based on the explicit kernel mapping of ELM and the V-matrix method. Though V-matrix method could capture the geometrical structure of training data, it tends to assign a higher weight to instance with smaller input value. In order to avoid this bias, a novel method called VI-ELM is proposed by minimizing both the regression error and the V-matrix weighted error simultaneously. Finally, experiment results on 12 real world benchmark datasets show the effectiveness of our proposed methods.     

The forecast of the postoperative survival time of patients suffered from non-small cell lung cancer based on PCA and extreme learning machine

In this paper, a new effective model is proposed to forecast how long the postoperative patients suffered from non-small cell lung cancer will survive. The new effective model which is based on the extreme learning machine (ELM) and principal component analysis (PCA) can forecast successfully the postoperative patients' survival time. The new model obtains better prediction accuracy and faster convergence rate which the model using backpropagation (BP) algorithm and the Levenberg-Marquardt (LM) algorithm to forecast the postoperative patients' survival time can not achieve. Finally, simulation results are given to verify the efficiency and effectiveness of our proposed new model.     

Pattern recognition of number gestures based on a wireless surface EMG system

Using surface electromyography (sEMG) signal for efficient recognition of hand gestures has attracted increasing attention during the last decade, with most previous work being focused on recognition of upper arm and gross hand movements and some work on the classification of individual finger movements such as finger typing tasks. However, relatively few investigations can be found in the literature for automatic classification of multiple finger movements such as finger number gestures. This paper focuses on the recognition of number gestures based on a 4-channel wireless sEMG system. We investigate the effects of three popular feature types (i.e. Hudgins’ time–domain features (TD), autocorrelation and cross-correlation coefficients (ACCC) and spectral power magnitudes (SPM)) and four popular classification algorithms (i.e. k-nearest neighbor (k-NN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and support vector machine (SVM)) in offline recognition. Motivated by the good performance of SVM, we further propose combining the three features and employing a new classification method, multiple kernel learning SVM (MKL-SVM). Real sEMG results from six subjects show that all combinations, except k-NN or LDA using ACCC features, can achieve above 91% average recognition accuracy, and the highest accuracy is 97.93% achieved by the proposed MKL-SVM method using the three feature combination (3F). Referring to the offline recognition results, we also implement a real-time recognition system. Our results show that all six subjects can achieve a real-time recognition accuracy higher than 90%. The number gestures are therefore promising for practical applications such as human–computer interaction (HCI).     

Evaluation of the effect of stent strut profile on shear stress distribution using statistical moments

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n = 34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.     

An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.     

基于混合泊松分布的新生突变识别算法

对个体而言,不经父母遗传而后天获得的突变称为新生突变,绝大多数癌症都起自新生突变。构建快速精确的变异识别算法将有助于对癌症的研究。然而,针对前期新生突变识别算法准确率不高,且耗时多等问题,本文引入了基于变异位点的先验概率分布模型,运用基于混合泊松分布的期望最大化（EM）算法对新生突变识别算法进行改进与优化,研究了有亲缘关系的新生突变的识别,并在识别精度与运算速度方面与已有算法进行对比。结果表明,基于混合泊松分布的 期望最大化算法在提高运算速度的同时降低了假阳性比率,具有良好的识别效果。     

Extreme Learning Machine-Based Classification of ADHD Using Brain Structural MRI Data

Background

Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1) Propose an ADHD classification model using the extreme learning machine (ELM) algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2) Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM) methods and analyze which brain segments are involved in ADHD.

Methods

High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc.) were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation.

Results

We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular.

Conclusion

Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.     

Liver vessel segmentation based on extreme learning machine

Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity.     

A comparison of position-specific score matrices based on sequence and structure alignments

Sequence comparison methods based on position-specific score matrices (PSSMs) have proven a useful tool for recognition of the divergent members of a protein family and for annotation of functional sites. Here we investigate one of the factors that affects overall performance of PSSMs in a PSI-BLAST search, the algorithm used to construct the seed alignment upon which the PSSM is based. We compare PSSMs based on alignments constructed by global sequence similarity (ClustalW and ClustalW-pairwise), local sequence similarity (BLAST), and local structure similarity (VAST). To assess performance with respect to identification of conserved functional or structural sites, we examine the accuracy of the three-dimensional molecular models predicted by PSSM-sequence alignments. Using the known structures of those sequences as the standard of truth, we find that model accuracy varies with the algorithm used for seed alignment construction in the pattern local-structure (VAST) > local-sequence (BLAST) > global-sequence (ClustalW). Using structural similarity of query and database proteins as the standard of truth, we find that PSSM recognition sensitivity depends primarily on the diversity of the sequences included in the alignment, with an optimum around 30-50% average pairwise identity. We discuss these observations, and suggest a strategy for constructing seed alignments that optimize PSSM-sequence alignment accuracy and recognition sensitivity.     

Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes.     

Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning

Background and objectiveBreast cancer, the most intrusive form of cancer affecting women globally. Next to lung cancer, breast cancer is the one that provides a greater number of cancer deaths among women. In recent times, several intelligent methodologies were come into existence for building an effective detection and classification of such noxious type of cancer. For further improving the rate of early diagnosis and for increasing the life span of victims, optimistic light of research is essential in breast cancer classification. Accordingly, a new customized method of integrating the concept of deep learning with the extreme learning machine (ELM), which is optimized using a simple crow-search algorithm (ICS-ELM). Thus, to enhance the state-of-the-art workings, an improved deep feature-based crow-search optimized extreme learning machine is proposed for addressing the health-care problem. The paper pours a light-of-research on detecting the input mammograms as either normal or abnormal. Subsequently, it focuses on further classifying the type of abnormal severities i.e., benign type or malignant.Materials and methodsThe digital mammograms for this work are taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mammographic Image Analysis Society (MIAS), and INbreast datasets. Herein, the work employs 570 digital mammograms (250 normal, 200 benign and 120 malignant cases) from CBIS-DDSM dataset, 322 digital mammograms (207 normal, 64 benign and 51 malignant cases) from MIAS database and 179 full-field digital mammograms (66 normal, 56 benign and 57 malignant cases) from INbreast dataset for its evaluation. The work utilizes ResNet-18 based deep extracted features with proposed Improved Crow-Search Optimized Extreme Learning Machine (ICS-ELM) algorithm.ResultsThe proposed work is finally compared with the existing Support Vector Machines (RBF kernel), ELM, particle swarm optimization (PSO) optimized ELM, and crow-search optimized ELM, where the maximum overall classification accuracy is obtained for the proposed method with 97.193% for DDSM, 98.137% for MIAS and 98.266% for INbreast datasets, respectively.ConclusionThe obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the automatic detection and classification of breast cancer.     

基于K-means聚类算法的叶螨图像分割与识别

【目的】叶螨(spider mite)是为害多种农作物的主要害虫,叶螨识别传统方法依靠肉眼,比较费时费力,为研究快速自动识别方法,引入计算机图像分析算法。【方法】该方法基于K-means聚类算法对田间作物上的叶螨图像进行分割与识别。【结果】对比传统RGB彩色分割方法,K-means聚类算法能够有效地对叶片上叶螨图像进行分割和识别。K-means聚类算法平均识别时间为3.56 s,平均识别准确率93.95%。识别时间 T 随图像总像素 Pi 的增加而增加。【结论】K-means聚类组合算法能够应用于叶螨图像分割与识别。     

Prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine

Papermaking wastewater accounts for a large proportion of industrial wastewater, and it is essential to obtain accurate and reliable effluent indices in real-time. Considering the complexity, nonlinearity, and time variability of wastewater treatment processes, a dynamic kernel extreme learning machine (DKELM) method is proposed to predict the key quality indices of effluent chemical oxygen demand (COD). A time lag coefficient is introduced and a kernel function is embedded into the extreme learning machine (ELM) to extract dynamic information and obtain better prediction accuracy. A case study for modeling a wastewater treatment process is demonstrated to evaluate the performance of the proposed DKELM. The results illustrate that both training and prediction accuracy of the DKELM model is superior to other models. For the prediction of the quality indices of effluent COD, the determinate coefficient of the DKELM model is increased by 27.52 %, 21.36 %, 10.42 %, and 10.81 %, compared with partial least squares, ELM, dynamic ELM, and kernel ELM, respectively.     

Recognizing shorter coding regions of human genes based on the statistics of stop codons.

With the quick progress of the Human Genome Project, a great amount of uncharacterized DNA sequences needs to be annotated copiously by better algorithms. Recognizing shorter coding sequences of human genes is one of the most important problems in gene recognition, which is not yet completely solved. This paper is devoted to solving the issue using a new method. The distributions of the three stop codons, i.e., TAA, TAG and TGA, in three phases along coding, noncoding, and intergenic sequences are studied in detail. Using the obtained distributions and other coding measures, a new algorithm for the recognition of shorter coding sequences of human genes is developed. The accuracy of the algorithm is tested based on a larger database of human genes. It is found that the average accuracy achieved is as high as 92.1% for the sequences with length of 192 base pairs, which is confirmed by sixfold cross-validation tests. It is hoped that by incorporating the present method with some existing algorithms, the accuracy for identifying human genes from unannotated sequences would be increased.