One-class extreme learning machines for gas turbine combustor anomaly detection

Heart failure is one of the leading causes of death worldwide and requires accurate and timely diagnosis to improve patient outcomes. However, early detection remains a significant challenge due to the complexity of clinical data, high dimensionality of features, and variability in patient conditions. Traditional clinical methods often fall short in identifying subtle patterns that indicate early stages of heart failure, motivating the need for intelligent computational techniques to support diagnostic decisions. This study aims to enhance predictive modeling for heart failure classification by comparing two supervised machine learning approaches: Extreme Learning Machine (ELM) and Hierarchical Extreme Learning Machine (HELM). The main contribution of this research is the empirical evaluation of HELM's performance improvements over conventional ELM using 10-fold cross-validation on a publicly available clinical dataset. Unlike traditional neural networks, ELM offers fast training by randomly assigning weights and analytically computing output connections, while HELM extends this with a multi-layer structure that allows for more complex feature representation and improved generalization. Both models were assessed based on classification accuracy and Area Under the Curve (AUC), two critical metrics in medical classification tasks. The ELM model achieved an accuracy of 73.95% ± 8.07 and an AUC of 0.7614 ± 0.093, whereas the HELM model obtained a comparable accuracy of 73.55% ± 7.85 but with a higher AUC of 0.7776 ± 0.085. In several validation folds, HELM outperformed ELM, notably reaching 90% accuracy and 0.9250 AUC in specific cases. In conclusion, HELM demonstrates improved robustness and discriminatory capability in identifying heart failure cases. These findings suggest that HELM is a promising candidate for implementation in clinical decision support systems. Future research may incorporate feature selection, hyperparameter optimization, and evaluation across multi-center datasets to improve generalizability and real-world applicability.

One-class classification (OCC) has attracted a lot of attentions for its strong applicability of outlier/anomaly detection in real-world applications. Among them, the mixed model based on the autoencoder (AE) is the commonly used OCC architecture. The recent random neural network based extreme learning machine (ELM) AE (ELM-AE) has been extended to OCC. But no constraints have been applied to the encoded feature in ELM-AE and the random parameters used in ELM-AE may result in meaningless features. In this paper, we propose a novel within-class scatter constraint based randomized AE (WSC-RAE) by introducing a constraint on the distribution of the encoded feature to restrict the solution space and thus avoid the meaningless features. The proposed WSC-RAE is then embedded into a multilayer one-class ELM (ML-OCELM) framework for OCC. Experiments on 17 benchmark datasets are carried out to demonstrate the superiority of WSC-RAE.

In the paper, the multi-class pattern classification using extreme learning machine (ELM) is studied. The study is based on either a series of ELM binary classifiers or a single ELM classifier. When using binary ELM classifiers, the multi-class problem is decomposed into two-class problem using the one-against-all (OAA) and one-against-one (OAO) schemes, which are named as ELM-OAA and ELM-OAO respectively for brevity. In a single ELM classifier, the multi-class problem is implemented with an architecture of multi-output nodes which is equal to the number of pattern classes. Their performance is evaluated using some multi-class benchmark problems and simulation results show that ELM-OAA and ELM-OAO requires fewer hidden nodes than the single ELM classifier. In addition ELM-OAO usually has similar or less computation burden than the single ELM classifier when the pattern class labels is not larger than 10.

Extreme Learning Machine (ELM) is an extension of feed forward Neural Network model with extremely learning capacity and good generalization capabilities. Generally, the classification performance of ELM highly focused on three parameters such as the input weight matrix, the value of bias and the number of hidden neurons presented. ELM randomly chooses the input weights and bias values to minimize the training time. The arbitrary selection of biases and the input weight produce an unforeseen result which causes training error and also produces lesser prediction accuracy. For improving efficiency of ELM training, Ant Colony Optimization with continuous domain algorithm (ACO R ) used to find the optimum input weight and hidden bias values for ELM. With the unequal distribution of classes in imbalanced data sets, ELM algorithms tussle to find good accuracy. ELM algorithm doesn't acquire the mandatory information about the minority class to make an accurate classification. To deal the issues of ELM, in this paper the hybrid algorithms Weighted ELM and Weighted ELM with ACO R are proposed. The main objective of weighted ELM is that the related weight value is computed and assigned for each training sample to increase the classification rate. ACO R method is also integrated with the weighted ELM to find the optimum input weight and bias to maximize the accuracy of classification task. The comparative analysis has been accomplished over Hepatitis dataset. Further, the experimental results clearly revealed that Weighted ELM with ACO R is superior to other proposed methods.

Unsupervised or semi-supervised learning algorithms are essential for solving practical clustering problems due to the real world data are mostly unlabeled or partially labeled. Some common challenges such as poor clustering accuracy and slow learning speed are still need to be solved. Recently, various extreme learning machine (ELM) based methods have been found in clustering due to its extremely fast learning speed and excellent approximation ability. In this paper, some brief introductions of general single-viewed (SV) and multi-viewed (MV) clustering algorithms are firstly included and then the investigation on combining ELM and MV algorithms are emphasized. Furthermore, combination of ELM and SV methods to solve MV clustering problems is attempted and satisfactory simulation results are obtained. Experiments are performed to compare the clustering performance in terms of effectiveness and efficiency among various of algorithms on a set of real world wireless distributed sensor networks (WDSNs) data. It is shown that the ELM based clustering algorithms achieve better clustering accuracy with less time consumption than conventional methods. Our attempts of the combining ELM and SV algorithms achieve comparable accuracy to ELM based MV algorithms but with even better time efficiency.

Extreme learning machine (ELM) is a well-known algorithm for single layer feedforward neural networks (SLFNs) and their learning speed is faster than traditional gradient-based neural networks. However, many of the tasks that ELM focuses on are single-label, where an instance of the input set is associated with one label. This paper proposes a new method for training ELM that will be capable of multi-label classification using the Canonical Correlation Analysis (CCA). The new method is named CCA-ELM. There are 4 steps in the training process: the first step is to compute any correlations between the input features and the set of labels using CCA, the second step maps the input space and label space to the new space, the third step uses ELM to classify and the last step is to map to the original input space. The experimental results show that CCA-ELM can improve ELM for classification on multi-label learning and its recognition performances are better than the other comparative algorithms that use the same standard CCA.

Complex power quality disturbances (CPQDs) classification can be regarded as a typical application of multi-label (ML) learning. In this study, we propose a new recognition method for CPQDs based on S-transform (ST) and a hybrid kernel function-based extreme learning machine (ELM) for ML learning (HKEML). The signal processing techniques S-transform is utilized to extract the distinctive features of the CPQDs. A novel ML classifier called HKEML is constructed by combining hybrid kernel function-based multiclass ELM and a thresholding learning method-based kernel ELM. Finally, a test study was conducted using Matlab synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with several recent state-of-the-art ML learning algorithms, HKEML achieved better classification performance but with greatly superior computational speed.

Clustering-based proxy measure for optimizing one-class classifiers

Extreme learning machine (ELM) was extended from the generalized single hidden layer feedforward networks where the input weights of the hidden layer nodes can be assigned randomly. It has been widely used for its much faster learning speed and less manual works. Considering the field of multi-label text classification, in this paper, we propose an ELM based algorithm combined with \(L_{21}\)-norm minimization of the output weights matrix called \(L_{21}\)-norm Minimization ELM, which not only fully inherits the merits of ELM but also facilitates group sparsity and reduces complexity of the learning model. Extensive experiments on several benchmark data sets show a more desirable performance compared with other common multi-label classification algorithms.

We consider the problem of intrusion detection in a computer network, and investigate the use of extreme learning machines (ELMs) to classify and detect the intrusions. With increasing connectivity between networks, the risk of information systems to external attacks or intrusions has increased tremendously. Machine learning methods like support vector machines (SVMs) and neural networks have been widely used for intrusion detection. These methods generally suffer from long training times, require parameter tuning, or do not perform well in multi-class classification. We propose a basic ELM method based on random features, and a kernel based ELM method for classification. We compare our methods with commonly used SVM techniques in both binary and multi-class classifications. Simulation results show that the proposed basic ELM approach outperforms SVM in training and testing speed, while the proposed kernel based ELM achieves higher detection accuracy than SVM in multi-class classification case.

The existence of missing data is a common fact in real applications which can significantly affect the data analysis process. In order to overcome this problem, many methods have been proposed in the literature. Extreme Learning Machine (ELM) has become a very popular research topic in machine learning and artificial intelligence areas due to its characteristics such as fast training procedure, good generalization and universal approximation capability. Although ELM has been successfully applied in different domains, its basic formulation cannot handle datasets with missing values properly. This paper presents a variant of the Extreme Learning Machine (ELM) for datasets with missing values. In the proposed method, probability distributions for the missing values are estimated using the expectation maximization (EM) algorithm, assuming that data is normally distributed. The Unscented Transform (UT) is used to estimate the values of the hidden layer outputs, and the weights of the output layer are assigned using the Moore-Penrose Pseudoinverse. Numerical experiments are carried out in order to evaluate the performance of the proposed method in four real world and two synthetic regression datasets. The results show that the proposed method presented a good performance in terms of Average Root-Mean-Squared Error (ARMSE).

This paper presents an approach to detection and segmentation of liver tumors in 3D computed tomography (CT) images. The automatic detection of tumor can be formulized as novelty detection or two-class classification issue. The method can also be used for tumor segmentation, where each voxel is to be assigned with a correct label, either a tumor class or nontumor class. A voxel is represented with a rich feature vector that distinguishes itself from voxels in different classes. A fast learning algorithm Extreme Learning Machine (ELM) is trained as a voxel classifier. In automatic liver tumor detection, we propose and show that ELM can be trained as a one-class classifier with only healthy liver samples in training. It results in a method of tumor detection based on novelty detection. We compare it with two-class ELM. To extract the boundary of a tumor, we adopt the semi-automatic approach by randomly selecting samples in 3D space within a limited region of interest (ROI) for classifier training. Our approach is validated on a group of patients' CT data and the experiment shows good detection and encouraging segmentation results.

Recently, there has been an ever-increasing interest in the study of data-driven analytics to predict hidden dangers in the cloud service-based coal mine production, with the purpose of the prevention of possible accidents. In this paper, to achieve the above prediction, a machine learning algorithm based on the single-hidden layer feed forward network (SLFN) using timeliness managing extreme learning machine (TMELM) is utilized. Compared with those traditional learning algorithms, extreme learning machine (ELM) has its unique feature of a higher generalization capability at a much faster learning speed. In addition, the timeliness managing ELM has been proposed by incorporating timeliness management scheme into ELM approach. Under the timeliness managing ELM scheme used to predict the hidden dangers, the newly incremental data could be prior to the historical data while maximizing the contribution of the newly increasing training data, since it may be more feasible that the incremental data can contribute reasonable weights to represent the current production situation in accordance with the practical analysis for accidents in coal mine production. The experimental results on the coal mines of Beijing show that by using timeliness managing ELM, the prediction accuracy of hidden danger can be improved with better stability compared with other similar machine learning methods.

Multiple faults are easily confused with single faults. In order to identify multiple faults more accurately, a highly efficient learning method is proposed based on a double parallel two-hidden-layer extreme learning machine, called DPT-ELM. The DPT-ELM method is a variant of an extreme learning machine (ELM). There are some issues with ELM. First, achieving a high accuracy requires too many hidden nodes; second, the direct connection between the input layer and the output layer is ignored. Accordingly, to deal with the above-mentioned problems, DPT-ELM extends the single-hidden-layer ELM to a two-hidden-layer ELM, which can achieve a desired performance with fewer hidden nodes. In addition, a direct connection is built between the input layer and the output layer. Since the input layer weights and the thresholds of the two hidden layers are determined randomly, this simplifies the improved model and shortens the calculation time. Additionally, to improve the signal to noise ratio (SNR), an adaptive waveform decomposition (AWD) algorithm is used to denoise the vibration signal. Then, the denoised signal is used to extract the eigenvalues by the time-domain and frequency-domain methods. Finally, the eigenvalues are input to the DPT-ELM classifier. In this paper, two groups of rolling bearing data at different speeds, which were collected from a real experimental platform, are used to test the method. Each set of data includes three single fault states, two complex fault states and a healthy state. The experimental results demonstrate that the DPT-ELM method achieves fast learning speed and a high accuracy. Moreover, based on 10-fold cross-validation, it proves to be an effective method to improve the accuracy with fewer hidden nodes.

One of the important assets in a company is customers. Customers determine the company's stability because they are source of income and determine the company's competitiveness. It shows the importance of predicting which customers have the potential to switch to another company. These predictions can be done using Machine Learning (ML). One of ML methods is the Extreme Learning Machine (ELM). The advantages of ELM compared to other methods are fast computing time, ease of use, and can reach a global optimum. However, ELM has weaknesses when solving problems with high-dimensional datasets, so feature selection is required. The Binary Bat Algorithm (BBA) is a swarm intelligence method that can be used to optimize ELM performance. The advantages of BBA compared to other are few parameters and much better in effectiveness or accuracy. This research was carried out with preprocessing data, training data and testing data. The research results showed that ELM-BBA is better than ELM and ELM-Binary Particle Swarm Optimization (BPSO) in evaluation metric values. However, ELM-BBA tended to be slower than ELM-BPSO. The best results on evaluation metrics achieved by ELM-BBA were 0.97, 0.97, 0.96, and 0.97 in accuracy, precision, recall, and F1 score, respectively.