Kernel Extreme Learning Machine Classifier Research Articles

Power quality disturbance signal classification in microgrid based on kernel extreme learning machine

AbstractThis paper presents a kernel extreme learning machine (KELM) integrated with the improved whale optimization algorithm (IWOA) to address the power quality disturbance (PQD) issue in microgrids. First, an adaptive variational mode decomposition method is employed to extract PQD signals in microgrids. Then, the IWOA is utilized to optimize the penalty factor and kernel function parameters for the KELM classifier model, thereby enhancing the performance of the classifier. Furthermore, the test results indicate that the proposed IWOA–KELM achieves high classification accuracy and rapid convergence for complex PQD signals.

Fault diagnosis of rolling bearing based on SEMD and ISSA-KELMC

Enhancing the operational reliability of rotary machinery relies significantly on the effective diagnosis of faults in rolling bearings. This study introduces an innovative method to improve the accuracy of fault diagnosis of rolling bearings during operation. First, we propose a sine empirical mode decomposition (SEMD) designed to effectively mitigate mode mixing and decompose the vibration signals of rolling bearings into a series of intrinsic mode functions. Subsequently, we constructed and optimized a kernel extreme learning machine classifier (KELMC) using the improved sparrow search algorithm (ISSA). Within ISSA, the opposition-based Learning method is refined and applied to enhance the optimization performance of the sparrow search algorithm. Finally, the paper presents a novel method for the fault diagnosis of rolling bearings based on SEMD and ISSA-KELMC, which can effectively extract the fault features and accurately recognize the fault types of rolling bearings by taking advantage of the SEMD and ISSA-KELMC. The effectiveness of the proposed method was verified through two simulation and fault diagnosis experiments. The results demonstrated the efficiency of the method in diagnosing faults in rolling bearings under both consistent and variable working conditions. This approach is valuable for fault diagnosis and condition monitoring of rotating machinery.

Machine learning based approach for modeling and forecasting of GPS–TEC during diverse solar phase periods

The ionosphere is a dispersive medium and it is the most important source of error affecting radio communications. As a result, precise improvements in ionospheric Total Electron Content (TEC) predictions and early warning alerts are critical for space-based radio communication and navigation systems services. In the present study, we examined the Global Positioning System (GPS)–derived TEC observations of Bangalore station (Geog. Lat. 13.02° N and Geog. Long. 77.57° E) during the 24th solar cycle, which extends the 11 years from 2009 to 2019. Based on Solar Radio Flux (sfu) and seasonal features, machine learning frameworks have been developed to forecast monthly/hourly GPS-TEC values. Motivated by the preliminary results, the problem of traditional approaches for predicting ionospheric GPS-TEC values and improvements using the machine learning approach is discussed. The Kernel extreme learning machine (KELM) model has characteristics of accuracy and good generalization performance compared with the traditional method of Holt-Winters (HW) and (Auto Regressive Moving Average (ARMA). The experimental results show that the statistical validations of the KELM model are superior to the other statistical models, with average Mean Absolute Error (MAE) and Root Mean Square Error values, which is of 0.94 and 1.305 TECU (KELM), 1.70 and 2.29 TECU (ARMA) and 2.91 and 3.86 TECU (HW) during different phases of the solar cycle. In addition, the proposed model is validated by comparison with global ionospheric models (International Reference Ionosphere (IRI) −2016 and Global Ionosphere Map (GIM). The results show that the proposed approach is superior to other alternatives using prediction accuracy. This paper outlines a machine learning framework for space weather predictions and recommends KELM classifier could be used for other query functions in Active Learning (AL) practice approaches and other datasets of the Global Navigation Satellite System (GNSS).

Correlation-based feature selection using bio-inspired algorithms and optimized KELM classifier for glaucoma diagnosis

Reduced computational time and cost, reduced skilled professional resources, and diagnostic accuracy have made medical diagnosis using computer aided systems (CAD) increasingly popular and can be comfortably employed in the diagnosis of many acute and chronic diseases in ophthalmology, cardiology, cancer detection, etc. There seems to be a growing necessity for the computational algorithms to be robust enough, to identify the abnormality in each of the cases, aiding in early diagnosis. In this paper, a glaucoma diagnostic approach based on the wrapper method employing bio-inspired algorithms, and a Kernel-Extreme Learning Machine (KELM) classifier is proposed. The bio-inspired algorithms are deployed to select feature sub-sets, generating three feature sub-sets from the pre-processed fundus images by adopting a correlation-based feature selection (CFS) approach. The selected features are utilized to train the salp-swarm optimization based KELM, which finds the optimal parameters of the KELM classifier network. The proposed methodology is evaluated on the public and private retinal fundus datasets containing 7280 images. The experimental outcome revealed that the system is able to attain a maximum overall accuracy of 99.61% with 99.89% sensitivity and 100% specificity. A 5-fold cross validation showed 98.78% accuracy ensuring a bias-free classification. Further, by experimenting on degraded images (Gaussian, salt-pepper noise images) of the original dataset, the model achieved extreme robustness with 99.3% accuracy. The proposed method is compared with other similar methods, which showed the efficiency of our method. The framework proposed can aid in making clinical decisions for various pathologies like lung infection, diabetic retinopathy, etc.

Bio-inspired ensemble feature selection (biefs) and kernel extreme learning machine classifier for breast cancer diagnosis

Breast cancer is a major disease identified in women, affecting 2.1 million women every year, and is the reason for most cancer-related mortality in women, as per the World Health Organization (WHO). For cancer researchers, accurately forecasting the life expectancy of breast cancer patients is a serious challenge. Machine Learning (ML) has acknowledged much interest in the hope of providing correct results, but due to irrelevant features, its modelling methodologies and prediction performance are still a difficulty. To solve this issue, Feature Selection (FS) was also done to verify whether comparable accuracy can be achieved even with lesser number of features or not. Bio-Inspired Ensemble Feature Selection (BIEFS) algorithm is introduced aimed at selecting a subset of features that increase the prediction performance of subsequent classification models while also simplifying their interpretability. BIEFS algorithm uses three feature selection methods such as Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO), Adaptive Mutation Butterfly Optimization Algorithm (AMBOA), and Adaptive Salp Swarm Algorithm (ASSA) and integrates their normalized outputs for getting quantitative ensemble importance. BIEFS algorithm depends upon the aggregation of multiple FS techniques by Pearson Correlation Coefficient (PCC).This BIEFS algorithm can improve the accuracy of analysis (benign and malignant).

A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine

Chemical processes usually exhibit complex, high-dimensional and non-Gaussian characteristics, and the diagnosis of faults in chemical processes is particularly important. To address this problem, this paper proposes a novel fault diagnosis method based on the Bernoulli shift coyote optimization algorithm (BCOA) to optimize the kernel extreme learning machine classifier (KELM). Firstly, the random forest treebagger (RFtb) is used to select the features, and the data set is optimized. Secondly, a new optimization algorithm BCOA is proposed to automatically adjust the network hyperparameters of KELM and improve the classifier performance. Finally, the optimized feature sequence is input into the proposed classifier to obtain the final diagnosis results. The Tennessee Eastman (TE) chemical process have been collected and used to verify the effectiveness of the proposed method. A comprehensive comparison and analysis with widely used algorithms is also performed. The results demonstrate that the proposed method outperforms other methods in terms of classification accuracy. The average diagnosis rate of 21 faults is found to be 89.32%.

Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities

AbstractSeveral deep learning techniques are utilized for classification of the masses in mammogram images. But the existing method doesnot provide sufficient accuracy . for breast masses classification in mammogram images. In this manuscript, an efficient breast cancer image classification framework utilizing the deep belief network (DBN) through chaotic krill herd optimization (CKHO) algorithm for classification of masses in mammogram images is proposed. Initially the input mammogram images are pre‐processed by altered phase preserving dynamic range compression (APPDRC) method for removing the unwanted noise and artifacts. Then these pre‐processed images are assumed with DBN for classifying that mass in the mammogram images into normal, benign, and malignant lesions. Generally, DBN does not reveal any acceptance of optimization techniques for computing optimal parameters that guarantee an accurate classification. Therefore in this work, proposed CKHO algorithm is employed for optimizing weight parameters of the self‐attention convolutional neural network (SACNN). The simulation process is performed under MATLAB platform. Finally, the proposed DBN with chaotic krill herd optimization algorithm (DBN‐CKHO) attains high accuracy 44.5%, 33.42%, 55.23%, 62.35%, and 43.42% when compared with the existing method such as extreme learning machine classifier with moth flame optimization (ELMC‐MFO), Kernel extreme learning machine classifier with Chaotic salp swarm algorithm (KELMC‐CSSA), Extreme learning machine classifier with fruit fly optimization algorithm (ELMC‐FOA), Kernel extreme learning machine classifier with grasshopper optimization algorithm (KELMC‐GOA) and deep neural network with non‐dominated sorting genetic algorithm‐based classification approach.

Emotion recognition in election day tweets using optimised kernel extreme learning machine classifier

ABSTRACT Today, microblogging has turned into a very well-known specialised device among web clients. A huge number of clients share ideas on various aspects of daily life. In this manner these sites are rich sources of information utilised with the goal of sentiment investigation. This investigation is essentially valuable as well as challenging since everybody consistently need to know the views of existing clients about an item or service. In this article, the most prominent microblogging platform ‘Twitter’ is selected for the investigation of sentiment classification and mining opinions. This is helpful for shoppers who need to enquire about items before purchase, or organisations that need to screen the open assumption of their brands. A unique approach is presented for classifying the feeling of Political Twitter messages into Happy, Extremely Happy, Sad, Extremely Sad or else Neutral. There are numerous devices that give computerised opinion investigation. The dataset used here is the Twitter corpus Dataset named 2016 Political Election day tweets gathered using the Twitty and Simulation apparatus utilised in this examination is PYTHON. Utilising the corpus, sentiment classifier named Kernel Extreme Learning Machine (KELM) optimised by Salp Swarm Algorithm (SSA) determines the class of political tweets as Happy, Extremely Happy, Sad, Extremely Sad and Neutral Sentiments. This work mainly focus on English tweets. The proposed KELM classifier performance is compared with existing classifier approaches and noted with highest accuracy which defines the effective nature of the classifier. Moreover, test evaluation demonstrate that the proposed procedure is effective and performs superior to recently proposed works.

Generalized Oppositional Moth Flame Optimization with Crossover Strategy: An Approach for Medical Diagnosis

In the original Moth-Flame Optimization (MFO), the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame, so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization problems. Therefore, in this work, a generalized oppositional MFO with crossover strategy, named GCMFO, is presented to overcome the mentioned defects. In the proposed GCMFO, GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate; crisscross search (CC) is adopted to promote the exploitation and/or exploration ability of MFO. The proposed algorithm’s performance is estimated by organizing a series of experiments; firstly, the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal problems. Secondly, GCMFO is applied to handle multilevel thresholding image segmentation problems. At last, GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases, including the appendicitis diagnosis, overweight statuses diagnosis, and thyroid cancer diagnosis. Experimental results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and accuracy. It also indicates that the presented GCMFO has a promising potential for application.

Selection of informative genes from high-dimensional cancerous data employing an improvised meta-heuristic algorithm

Identification of the most prominent genes with high classification accuracy in the high-dimensional cancerous data has remained an emerging challenge for machine learning researchers. The selection of informative genes is a non-deterministic polynomial-time (NP-Hard) issue. Therefore, a scope always lies in employing new algorithms in this field. In this work, an improved version of a meta-heuristic algorithm, Chaotic Jaya (CJaya), is hybridized with Kernel Extreme Learning Machine (KELM), called as CJaya-KELM, to select the most informative genes and classify the high-dimensional cancerous data. Initially, the Fisher score technique is used to pre-select the informative genes. Then, the CJaya algorithm is employed for both selecting key genes and optimizing the parameters of the KELM classifier. To evaluate the designed model, six cancerous datasets are considered. Here, the designed model CJaya-KELM, has been compared with particle swarm optimization hybridized KELM (PSO-KELM), genetic algorithm hybridized KELM (GA-KELM), and Jaya hybridized KELM (Jaya-KELM) models. Moreover, a comparison between the suggested model with other ten existing models is demonstrated here. Some performance metrices like accuracy in tenfold cross-validation method, the number of selected genes, sensitivity, F-measure, specificity, and Matthews correlation coefficient (MCC) are applied to measure the efficiency of the suggested model. The CJaya-KELM approach resulted in the highest accuracy, sensitivity and specificity in Colon tumor (.9677, .9714, .963), Leukemia (.99, .9756, 1), Ovarian cancer (1, 1, .9892), Lymphoma-3 (.9971, 1, .9583), ALL-AML-3 (.9961, .9767, 1) and SRBCT (.998, 1, .9583) datasets respectively. The experimental results reveal that the suggested model CJaya-KELM is outperforming.

Two-step multi-view and multi-label learning with missing label via subspace learning

In multi-view and multi-label learning, each example can be represented by multiple data view features and annotated with a set of discrete non-exclusive labels. Missing label learning is an important branch of multi-label learning, which can handle incomplete labels with annotations. Previous work on multi-label learning with missing labels mainly considered data in a single view representation. Based on intuitive understanding, we propose a Two-step Multi-view and Multi-label Missing Label learning optimization solution(TM3L). The first step is to solve the multi-view learning problem by finding the data representation of the common low-dimensional space of all views through subspace learning. While fully considering the complementary information between multiple views, the different degrees of contribution combined with different views are weighted differently. The second step is to solve the multi-label missing label learning problem by using the label matrix completion method in combination with the kernel extreme learning machine classifier. The kernel extreme learning machine can effectively enhance the robustness of the algorithm to missing labels. The experimental results and analysis on multiple benchmark multi-view and multi-label data sets verify the effectiveness of TM3L compared with the state-of-the-art solutions.

Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis

Bankruptcy prediction is a crucial application in financial fields to aid in accurate decision making for business enterprises. Many models may stagnate to low-accuracy results due to the uninformed choice of parameters. This paper presents a forward-thinking bankruptcy prediction model based on kernel extreme learning machine (KELM), which proposes a new efficient version of a fruit fly optimization (FOA) algorithm called LSEOFOA, to evolve and harmonize the penalty and the kernel parameter in KELM. The upgraded version of FOA is conceptualized based on three reorganizations. The first attempt is to include Levy's flight for improving exploration inclinations, and the second is based on slime mould algorithm (SMA) for avoiding premature convergence and enhancing the stability of the exploration and exploitation patterns. As the last modification, we utilized the elite opposition-based learning for accelerating the convergence. The algorithmic trends of this optimizer are verified, and then, it is verified on a bankruptcy prediction module. Therefore, to further demonstrate the superiority of the LSEOFOA method, comparison studies are performed using the conventional FOA and other variants of FOA and a set of advanced algorithms including EBOwithCMAR. Experimental results for every optimization task demonstrate that LSEOFOA can provide a high-performance and self-assured tradeoff between exploration and exploitation. Also, the developed KELM classifier is utilized for bankruptcy prediction, and its optimal parameters set are revealed by the proposed FOA. The effectiveness of the LSEOFOA-KELM model is rigorously evaluated using a financial dataset and comparison with KELM-based models with other competitive optimizers such as LSHADE-RSP. Overall research findings show that the proposed model is superior in terms of classification accuracy, Matthews correlation coefficient, sensitivity, and specificity. Towards more evolutionary and efficient prediction models, the proposed LSEOFOA-KELM prediction model can be regarded as a promising warning tool for financial decision making, with successful performance in bankruptcy prediction. Interested readers to the idea and related material of LSEOFOA-KELM can find the designed public web service at https://aliasgharheidari.com. Also, the info and source codes of the slime mould algorithm (SMA) in python, matlab and other languages are shared publicly at https://aliasgharheidari.com/SMA.html.

Early determination of mildew status in storage maize kernels using hyperspectral imaging combined with the stacked sparse auto-encoder algorithm

The early detection the mildew status of maize kernels caused by fungal infection during storage is essential. Hyperspectral imaging (HSI) combined with the deep stacked sparse auto-encoders (SSAE) algorithm was employed to recognize the early mildewed degree of kernels. HSI data of kernels were collected in the spectral range of 400–1000 nm and the mildew status was classified into four grades (health, mild mildew, moderate mildew, and heavy mildew) using the measured fungal spores as the reference. The nonlinear features of the spectra were represented using the SSAE network, and the key wavelengths were selected by variable combination population analysis and random frog methods, which were combined with four classifiers-Softmax, kernel extreme learning machine (KELM), ELM, and support vector machine-to establish models for identifying the mildew grades of kernels. The comparative results indicated that SSAE combined with KELM classifier to build the SSAE-KELM model had better identification ability than other models with high total correct recognition rate of 97.36% and 96.84% for the training and testing sets, respectively. The sensitivity and specificity were 0.92–1. The different grades were visualized on the images of maize kernels based on pixels-level and object-level. The current results demonstrated that HSI with SSAE-KELM model performed well in the early detection of mildew status of maize kernel, and can be applied as the reference method for batch and online detection of mildew kernels to improve grain storage security.

Short‐term traffic flow prediction of road network based on deep learning

Due to the fact that existing traffic flow forecasting methods cannot completely reflect the real time traffic situation of the road network, a new method of short-term traffic flow prediction is proposed based on deep learning in this study. Firstly, in order to improve the efficiency of data processing, a method of road network data compression is proposed based on correlation analysis and CX decomposition. Secondly, the traffic flow data are divided into trend term and random fluctuation term by spectral decomposition method, and the influence of trend term on prediction accuracy is removed. Finally, by combining a deep belief network model and a kernel extreme learning machine classifier as the prediction model, the essential characteristics of the traffic flow data are extracted by using DBN at the bottom of the network, and the extracted results are input into the kernel extreme learning machine to predict the traffic flow. The actual regional road network traffic flow data are tested to verify the effectiveness of the proposed short-time network traffic flow forecasting method. The results show that the proposed method can not only save 90% of the running time but also the average prediction accuracy of each road section can reach 92%.

Dual-layer kernel extreme learning machine for action recognition

In this paper, we propose a simple yet effective method for video based action recognition referred to as dual-layer kernel extreme learning machine (DKELM). Our approach takes advantages of both early and late fusion techniques into a unified framework. In particular, the first layer in DKELM adopts linear kernel extreme learning machine (KELM) on handcrafted feature kernel, deep-learned feature kernel, and the fused kernel to provide various perspectives about the video. The second layer trains a radial basis function based KELM classifier on different fusion scores obtained from the first layer to predict the final action class label. Finally, we empirically show the superior performance of DKELM, both in terms of accuracy and computational time, over some state-of-the-art human action recognition methods on two large-scale datasets.

Mexican Hat Wavelet Kernel ELM for Multiclass Classification.

Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems. Moreover, the validity of the Mexican Hat wavelet as a kernel function of ELM is rigorously proved. Experimental results on different data sets show that the performance of the proposed classifier is significantly superior to the compared classifiers.

A kernel extreme learning machine algorithm based on improved particle swam optimization

Kernel extreme learning machine (KELM) increases the robustness of extreme learning machine (ELM) by turning linearly non-separable data in a low dimensional space into a linearly separable one. However, the internal power parameters of ELM are initialized at random, causing the algorithm to be unstable. In this paper, we use the active operators particle swam optimization algorithm (APSO) to obtain an optimal set of initial parameters for KELM, thus creating an optimal KELM classifier named as APSO-KELM. Experiments on standard genetic datasets show that APSO-KELM has higher classification accuracy when being compared to the existing ELM, KELM, and these algorithms combining PSO/APSO with ELM/KELM, such as PSO-KELM, APSO-ELM, PSO-ELM, etc. Moreover, APSO-KELM has good stability and convergence, and is shown to be a reliable and effective classification algorithm.

Hyperspectral image classification using FPCA-based kernel extreme learning machine

In this paper, the capabilities of functional data feature extraction technique are combined with the advantages of kernel extreme learning machine (KELM), to develop an effective hyperspectral image (HSI) classification method. In the proposed method, the hyperspectral pixels are firstly represented by functions. Each pixel in the HSI is processed from the perspective of function rather than high-dimensional vector. These functional representations are transformed to a lower dimensionality feature space using functional principal components analysis (FPCA). And then the obtained lower dimensional representations are processed by a multiclass KELM classifier. Experimental results on two HSI datasets show that the proposed method provides a relatively promising performance compared with other methods.