Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder

It is well-known that solar power depends on many complex parameters such as humidity, radiation, temperature, dust and wind speed. In order to cope with these complex structures and to provide an accurate forecast, the development of reliable and effective forecasting methodology is very significant. In this study, an Artificial Neural Network (ANN)-based system has been developed to forecast very short-term (2 to 4-h) power output of a grid tied Photovoltaic Power Plant (PVPP). An algorithm called Extreme Learning Machine (ELM) has gained increasing interest through its extremely fast learning and good generalization capability. The Kernel Extreme Learning Machine (KELM) which is the improved version of the ELM is proposed to develop a very short-term PVPP power forecast system. The most important feature of KELM is that it has less adjustable parameters and better generalization ability when compared with classical ELM. Experimental studies have been carried out on a grid tied PVPP that has 1 (MW) installed power capacity. The inputs of the KELM-based forecast system are selected as solar power, humidity, radiation and temperature. All data are divided into four parts to analyze the effect of seasons on the performance of the proposed forecast system. The comparison studies are carried out to clearly observe the forecast ability and performance of the KELM. From the experimental studies of the winter season which has not symmetric diurnal mean power curve, Correlation Coefficients (R) values of KELM are calculated as 0.896 for 2-h ahead, 0.865 for 3-h ahead, 0.833 for 4-h ahead. Those of ELM and (Levenberg Marquardt) LM are 0.866–0.812 for 2-h ahead, 0.831–0.669 for 3-h ahead, 0.799–0.502 for 4-h ahead, respectively. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of KELM for winter season are 12.08%-6.14% for 2-h ahead, 14.06%-7.43% for 3-h ahead, 15.33%-7.74% for 4-h ahead, respectively. While these values of ELM are obtained as 13.82%-7.05% for 2-h ahead, 15.52%-8.77% for 3-h ahead, 17.63%-9.33% for 4-h ahead. The RMSE and MAE values of LM are 17.90%-9.14% for 2-h ahead, 20.04%-9.85% for 3-h ahead, 21.22%-10.68% for 4-h ahead. According to the obtained results, it is clearly seen that KELM provides a more powerful and reliable forecasting performance.

Spectra data classification with kernel extreme learning machine

Human activity recognition (HAR) is a main research field of context-aware computing; the performance of HAR mainly depends on the feature extraction method and classification algorithm. Extreme learning machine (ELM) is a single hidden layer neural network, which has better classification and generalization ability. However, ELM is not suitable for feature extraction. Deep learning is a hot research field as it can automatically extract significant features from raw data. In this paper, we propose an approach: an ELM-based deep model, which combined convolutional neural network (CNN), multilayer ELM (ML-ELM) as feature extractor, and used kernel ELM (KELM) as classifier. We used CNN and ML-ELM to extract significant features, and used KELM to achieve stable performance. The performance of proposed approach is validated on two public HAR datasets, and the experimental results show that the proposed approach is effective.

This paper proposes two deep learning modes which combine sparse auto-encoder with extreme learning machine (ELM) and kernel extreme learning machine (KELM), namely as Stacked Sparse Auto-encoder-Extreme Learning Machine and Stacked Sparse Auto-encoder-Kernel Extreme Learning Machine. The proposed models are applied to the image recognition task. To learn features from the original input data, SSAE with deep architecture is employed. Then, to construct a unified neural network learning model, ELM and KELM are selected to classify the extracted features. To evaluate the performance of the two proposed models, we carry out experiments on three different image data sets respectively. The results show that the proposed models’ performance can be not only superior to the shallow architecture models of Support Vector Machine, ELM and KELM but also better than the deep architecture models, such as Stacked Auto-encoder, Deep Belief Network and Stacked Denoising Auto-encoder.

This paper presents an evaluation of the Extreme Learning Machine (ELM), Kernel Extreme Learning Machine (KELM), and the Kernel Extreme Learning Machine optimized by the Whale Optimization Algorithm (WOA-KELM) in the context of fruit image classification. Fruits play a crucial role in everyday life, and the application of machine learning for fruit classification can significantly improve efficiency. The ELM, a single hidden layer feedforward neural network, is the foundation for this comparison. The KELM, an evolution of the ELM, incorporates a kernel function to map input data into a high-dimensional feature space, thereby enhancing performance. The development of WOA-KELM involves optimizing the regularization parameter in KELM and the width parameter of the radial basis function through the Whale Optimization Algorithm, further enhancing its capabilities. In classifying twelve types of fruit images, the ELM achieved a classification accuracy of 77%. In contrast, the WOA-KELM, as proposed in this paper, significantly outperforms with a remarkable classification accuracy of 92%.

This paper addresses the analysis of the problem of combining Infinite Kernel Learning (IKL) approach and Extreme Learning Machine (ELM) structure. ELM represents a novel and promising alternative to Neural Networks, for its simplicity in implementation and high efficiency, especially concerning convergence and generalization performance. A currently underdeveloped topic concerning ELM implementation is given by the optimization process of base kernels: choosing different kernel combinations may lead to very dissimilar performance results. An innovative ELM approach using a combination of multiple kernels has been proposed in Liu et al. As a change of paradigm, we are interested in using an infinite set of base kernels, defining in this way an original ELM based algorithm called Infinite Kernel Extreme Learning Machine (IK-ELM). About that, a novel 3-step algorithm combining IKL and ELM is proposed. Finally, a brief analysis about further possible directions is discussed.

This paper considers the accident images and develops a deep learning method for feature extraction together with a mixture of experts for classification. For the first task, the outputs of the last max-pooling layer of a Convolution Neural Network (CNN) are used to extract the hidden features automatically. For the second task, a mixture of advanced variations of Extreme Learning Machine (ELM) including basic ELM, constraint ELM (CELM), On-Line Sequential ELM (OSELM) and Kernel ELM (KELM), is developed. This ensemble classifier combines the advantages of different ELMs using a gating network and its accuracy is very high while the processing time is close to real-time. To show the efficiency, the different combinations of the traditional feature extraction and feature selection methods and the various classifiers are examined on two kinds of benchmarks including accident images’ data set and some general data sets. It is shown that the proposed system detects the accidents with 99.31% precision, recall and F-measure. Besides, the precisions of accident-severity classification and involved-vehicle classification are 90.27% and 92.73%, respectively. This system is suitable for on-line processing on the accident images that will be captured by Unmanned Aerial Vehicles (UAV) or other surveillance systems.

Recently, multilayer extreme learning machine with subnetwork nodes and cost sensitive learning was applied to representation learning and classification. However, the existing ML-ELM methods suffer from several drawbacks: 1) manual tuning for the number of hidden nodes in each layer will influent the training speed of the model, the best optimal determine approach for the number of hidden nodes is still for probing; 2) random projection of the input weight and bias in each layer still suboptimal and the network structure hard to reach best generalization performance. Inspired by the Subnetwork nodes and cost sensitive methods, using multiple hidden output matrix, a novel ELM method which called Multilayer Incremental Hybrid Cost Sensitive Extreme Learning Machine with Multiple Hidden Output Matrix and Subnetwork Hidden Nodes is proposed in this paper. Whose contributions are 1) a hybrid hidden nodes optimization methods is given which based on ant clone method and multiple grey wolf optimization method; 2) a multiple hidden layer output matrices are being using through weighted calculation of different output matrices which can optimize the network architecture effectively; 3) a novel ELM structure is proposed by merging the merits of Cost Sensitive Extreme Learning Machine and Subnetwork Hidden Nodes, while the regularized parameter $C$ is not sensitive to the generalization performance. Extensive experiments results show that the proposed method achieves better performance than existing state-of-art ELM methods.

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

This paper proposes a novel artificial neural network called Parallel Layer Perceptron Fast Learning Network (PLP-FLN). In PLP-FLN, a parallel single hidden layer feed-forward neural network is added on the basis of Fast Learning Network (FLN) which is an improved Extreme Learning Machine (ELM). Input weights and hidden layer biases are randomly generated. The weights connect the output nodes and the input nodes, and the weights connect the output nodes and the hidden nodes are analytically determined based on least squares methods. In order to test the PLP-FLN validity, this paper compared it with ELM, FLN, Kernel ELM and Incremental ELM through 12 regression applications and 7 classification problems. By comparing the experimental results, it shows that the PLP-FLN with much more compact networks have demonstrated better approximations, classification performances and generalization ability.

The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

In this paper, we explore the potential of kernelized extreme learning machine (KELM) for efficient diagnosis of Parkinson’s disease (PD). In the proposed method, the key parameters in KELM are investigated in detail. With the obtained optimal parameters, KELM manages to train the optimal predictive models for PD diagnosis. In order to further improve the performance of KELM models, feature selection techniques are implemented prior to the construction of the classification models. The effectiveness of the proposed method has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the ROC (receiver operating characteristic) curve (AUC).KeywordsExtreme learning machineFeature SelectionParkinson’s disease diagnosis

Fast kernel extreme learning machine for ordinal regression

Since it is easy to overfit due to the long training time of the fault diagnosis model for machinery. Introducing the idea of autoencoder (AE) into the wavelet extreme learning machine (WELM) and then stacking to form WELM-AE can convert the underlying fault features to more abstract and advanced ones. And then the adaptive boosting kernel extreme learning machine (Adaboost-KELM) is used as the top-level classifier for fault recognition. The experimental results verify the feasibility of the proposed algorithm in the fault diagnosis of tamping machine with the characteristics of the fast training speed of the extreme learning machine, and a higher accuracy rate than back propagation (BP), support vector machine (SVM), stacked autoencoder (SAE), and convolutional neural networks (CNN).

In the kernel extreme learning machine, a single global kernel function or a local kernel function can not balance the contradiction between fitting and generalization well, so replace the single kernel function with a mixed kernel function to improve the learning and generalization ability of the model. Based on this, a Multi-Kernel Extreme Learning Machine (MKELM) algorithm was proposed. Considering the parameter sensitivity characteristics of the MKELM, the artificial fish swarm algorithm (AFSA) was used to optimize the parameters. In summary, a hybrid algorithm was proposed and applied to the field of transformer fault diagnosis. Compared with the extreme learning machine algorithm of single kernel function and SVM algorithm, the diagnostic accuracy of this algorithm is higher.