Performance Of Extreme Learning Machine Research Articles

Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals.

Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions using surface Electromyography (sEMG) during continuous reach-to-grasp movements, involving multiple Degrees of Freedom(DoFs). This study explores feature extraction methods based on time domain and autoregressive models to evaluate ELM performance under different conditions. The experimental setup encompassed variations in neuron size, time windows, validation with each muscle, increase in the number of features, comparison with five conventional ML-based classifiers, inter-subjects variability, and temporal dynamic response. To evaluate the efficacy of the proposed ELM-based method, an openly available sEMG dataset containing data from 12 participants was used. Results highlight the method's performance, achieving Accuracy above 85%, F-score above 90%, Recall above 85%, Area Under the Curve of approximately 84% and compilation times (computational cost) of less than 1ms. These metrics significantly outperform standard methods (p < 0.05). Additionally, specific trends were found in increasing and decreasing performance in identifying specific tasks, as well as variations in the continuous transitions in the temporal dynamics response. Thus, the ELM-based method effectively identifies continuous reach-to-grasp motions through myoelectric data. These findings hold promise for practical applications. The method's success prompts future research into implementing it for more reliable and effective Human-Machine Interface (HMI) control. This can revolutionize real-time upper limb rehabilitation, enabling natural and complex Activities of Daily Living (ADLs) like object manipulation. The robust results encourages further research and innovative solutions to improve people's quality of life through more effective interventions.

Improving performance of extreme learning machine for classification challenges by modified firefly algorithm and validation on medical benchmark datasets

Improving performance of extreme learning machine for classification challenges by modified firefly algorithm and validation on medical benchmark datasets

Improving extreme learning machine model using deep learning feature extraction and grey wolf optimizer: Application to image classification

The Extreme Learning Machine (ELM) is a highly efficient model for real-time network retraining due to its fast learning speed, unlike traditional machine learning methods. However, the performance of ELM can be negatively impacted by the random initialization of weights and biases. Moreover, poor input feature quality can further degrade performance, particularly with complex visual data. To overcome these issues, this paper proposes optimizing the input features as well as the initial weights and biases. We combine both Convolutional Neural Network (CNN) and Convolutional AutoEncoder (CAE) extracted features to optimize the quality of the input features. And we use our hybrid Grey Wolf Optimizer-Multi-Verse Optimizer (GWO-MVO) metaheuristic for initializing weights and biases by applying four fitness functions based on: the norm of the output weights, the error rate on the training set, and the error rate on the validation set. Our method is evaluated on image classification tasks using two benchmark datasets: CIFAR-10 and CIFAR-100. Since image quality may vary in real-world applications, we trained and tested our models on the dataset’s original and noisy versions. The results demonstrate that our method provides a robust and efficient alternative for image classification tasks, offering improved accuracy and reduced overfitting.

An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis

AbstractIntegrating machine learning techniques into medical diagnostic systems holds great promise for enhancing disease identification and treatment. Among the various options for training such systems, the extreme learning machine (ELM) stands out due to its rapid learning capability and computational efficiency. However, the random selection of input weights and hidden neuron biases in the ELM can lead to suboptimal performance. To address this issue, our study introduces a novel approach called modified Harris hawks optimizer (MHHO) to optimize these parameters in ELM for medical classification tasks. By applying the MHHO‐based method to seven medical datasets, our experimental results demonstrate its superiority over seven other evolutionary‐based ELM trainer models. The findings strongly suggest that the MHHO approach can serve as a valuable tool for enhancing the performance of ELM in medical diagnosis.

An Extreme Learning Machine Approach for Forecasting the Wholesale Price Index of Food Products in India

Precise food price forecasting is crucial for any country, and searching for appropriate approach(s) from an assortment of available strategies toward this objective is an open problem. The current Indian Wholesale Price Index (WPI) series contains sixty individual food items in the 'manufacture of food product' category. This work considered the monthly data from April 2011 to June 2022, i.e., one hundred thirty-five months' data of these sixty WPIs. The researchers extracted the linearity, curvature, and autocorrelation features for each WPI. The curvature and linearity-based grouping of these WPIs revealed that the WPIs are heterogeneous. This work proposed an extreme learning machine (ELM) approach for forecasting these WPIs. The present work employed the following twenty-two time-series forecasting techniques: six standard methods (Auto ARIMA, TSLM, SES, DES, TES, and Auto ETS), five neural networks (Auto FFNN, Auto GRNN, Auto MLP, Auto ELM, and proposed ELM), and eleven state-of-art techniques (two ARIMA-ETS based ensembles, an ARIMA-THETAF-TBATS based ensemble, one MLP, and seven LSTM-based models) to identify the best forecasting approach for these WPIs. For the majority of WPIs, the offered ELM attained suitable performance in the case of fifteen months of out-of-sample forecasting. Nearly eighty-seven percent of cases achieved high accuracy (MAPE ≤ ten) and outshined others. Upon accuracy comparison, both forecast-MAPE and forecast-RMSE, between the proposed ELM and others, this paper observed that the proposed ELM's performance is more favorable. This paper's findings imply that the proposed ELM is a promising prospect to offer accurate forecasts of these sixty WPIs.

Extreme learning machine model with honey badger algorithm based state-of-charge estimation of lithium-ion battery

Accurate state-of-charge (SOC) detection was still a challenging task to complete due to complex battery dynamics and constantly changing external conditions. The formula for SOC was difficult to determine since external parameters including voltage, current, temperature, and battery arrangement were complex. Also the methods for estimating SOC that were already in use were not always appropriate for the same car operating in various road and climatic conditions. In all situations, the conventional methodologies did not deliver an accurate estimation performance. Here, a unique optimization-based Extreme Learning Machine (ELM) was created to accurately determine a battery's SOC and enhance the operation and safety of battery systems. A lithium ion battery was first created, and data on its current, voltage, SOC, capacity, duration, and discharge rate were gathered to produce a real-time dataset at several temperatures, including 00,250 and 450. The dataset underwent additional pre-processing to standardize the values and enhance the accuracy of the data. To determine the precise state of the battery, these pre-data were loaded into the ELM model. However, the performance of ELM was significantly influenced by the length of training and the number of neurons in a hidden layer. An advanced Honey Badger Optimization Algorithm (HBA) was used to choose the appropriate hidden neurons and increase the estimation accuracy in order to overcome this problem. The proposed SOC estimation model provides 97% accuracy in the FUDS drive cycle and 99% accuracy in the US06 drive cycle. The proposed model provides a well performance for estimating SOC in lithium-ion battery at various temperature, also the proposed model was fit for real time implementation.

An optimized extreme learning machine‐based novel model for bearing fault classification

AbstractThis work addresses the rolling element bearing (REB) fault classification problem by tackling the issue of identifying the appropriate parameters for the extreme learning machine (ELM) and enhancing its effectiveness. This study introduces a memetic algorithm (MA) to identify the optimal ELM parameter set for compact ELM architecture alongside better ELM performance. The goal of using MA is to investigate the promising solution space and systematically exploit the facts in the viable solution space. In the proposed method, the local search method is proposed along with link‐based and node‐based genetic operators to provide a tight ELM structure. A vibration data set simulated from the bearing of rotating machinery has been used to assess the performance of the optimized ELM with the REB fault categorization problem. The complexity involved in choosing a promising feature set is eliminated because the vibration data has been transformed into kurtograms to reflect the input of the model. The experimental results demonstrate that MA efficiently optimizes the ELM to improve the fault classification accuracy by around 99.0% and reduces the requirement of hidden nodes by 17.0% for both data sets. As a result, the proposed scheme is demonstrated to be a practically acceptable and well‐organized solution that offers a compact ELM architecture in comparison to the state‐of‐the‐art methods for the fault classification problem.

Domain adaptation deep learning and its T-S diagnosis networks for the cross-control and cross-condition scenarios in data center HVAC systems

Deep learning has the inspiring potential for artificial intelligence (AI) control in data centers of smart city. However, deep learning-based method is limited when the application scenarios are cross-control or cross-conditions. This paper proposes a novel domain adaptation deep learning model and Teacher-Student (T-S) networks to diagnose the faults of Heating, Ventilation and Air Conditioning (HVAC) systems under cross-control modes and cross-conditions. Firstly, domain adaptation deep belief network (DBN) model is presented to enhance the cross-control generalization performance of extreme learning machine (ELM) and K-nearest neighbor (KNN) diagnosis model. Secondly, the Teacher-Student networks are constructed. With the weights learned from the DBN model in Teacher Network, the cross-conditions capacity is improved for DELM model in the Student Network. Moreover, both the single and multiple faults are experimented and validated, which includes refrigerant leakage, reduced condenser water flow and reduced evaporator flow. The experimental results show that the domain adaptation deep learning model and its T-S diagnosis networks can reach the satisfied generalization performance. For the cross-control tests, the Accuracy of proposed method can reach 91%, 89% and 80% for fault-free, REW and RCW faults. For the cross-temperature and cross-load tests, the proposed method also obtained the high Accuracy of 98% and 88%, respectively.

Ultra-short-term PV power prediction using optimal ELM and improved variational mode decomposition

The development of photovoltaic (PV) power forecast technology that is accurate is of utmost importance for ensuring the reliability and cost-effective functioning of the power system. However, meteorological factors make solar energy have strong intermittent and random fluctuation characteristics, which brings challenges to photovoltaic power prediction. This work proposes, a new ultra-short-term PV power prediction technology using an improved sparrow search algorithm (ISSA) to optimize the key parameters of variational mode decomposition (VMD) and extreme learning machine (ELM). ISSA’s global search capability is enhanced by levy flight and logical chaotic mapping to search the optimal number of decomposition and penalty factor of VMD, and VMD adaptively decomposes PV power into sub-sequences with different center frequencies. Then ISSA is used to optimize the initial weight and threshold of ELM to improve the prediction performance of ELM, the optimized ELM predicts each subsequence and reconstructs the prediction results of each component to obtain the final result. Furthermore, isolated forest (IF) and Spearman correlation coefficient (SCC) are respectively used in the data preprocessing stage to eliminate outliers in the original data and determine appropriate input features. The prediction results using the actual data of solar power plants show that the proposed model can effectively mine the key information in the historical data to make more accurate predictions, and has good robustness to various weather conditions.

Accurate visible light positioning technique using extreme learning machine and meta-heuristic algorithm

Since input weights and hidden biases affect the overall performance of extreme learning machine (ELM) based on a single-hidden layer, two meta-heuristic algorithms of grey wolf optimizer (GWO) and particle swarm optimization (PSO) are adopted to optimize the input weights and hidden biases of ELM respectively in order to obtain higher classification accuracy. Namely, ELM-GWO and ELM-PSO are two enhanced ELM algorithms. Compared with the other existing techniques such as the traditional ELM and adaptive boosting (AdaBoost), the positioning performance of two enhanced ELM algorithms is analyzed by simulation and experiment in visible light positioning (VLP) systems. The experimental results show that the probabilities of two-dimensional (2D) positioning error being less than 10 cm for ELM, ELM-GWO, ELM-PSO, and AdaBoost are 84.25%, 90.25%, 93.25%, and 19.75%, respectively. And the probabilities of three-dimensional (3D) positioning error being less than 10 cm for ELM, ELM-GWO, ELM-PSO, and AdaBoost are 45.17%, 84.67%, 80.33%, and 16%, respectively. Both simulation and experimental results show that two enhanced ELM algorithms have better positioning performance and robustness. In addition, although increasing the number of iterations and the number of hidden neurons can effectively reduce the positioning error, the computational complexity is relatively high in large-scale fingerprint positioning scenarios. Therefore, a hybrid positioning method combining enhanced ELM and region division is further proposed. Both simulation and experimental results show that the proposed hybrid method can significantly improve the positioning accuracy while reducing the computational complexity.

Data Stream Classification Based on Extreme Learning Machine: A Review

Many daily applications are generating massive amount of data in the form of stream at an ever higher speed, such as medical data, clicking stream, internet record and banking transaction, etc. In contrast to the traditional static data, data streams are of some inherent properties, to name a few, infinite length, concept drift, multiple labels and concept evolution. Among all the data mining tasks, classification is one of the basic topics in data stream mining and has gained more and more attentions among different research communities. Extreme Learning Machine (ELM) has drawn much interests in data classification due to its high efficiency, universal approximation capability, generalization ability, and simplicity, which have greatly inspired the development of many ELM-based algorithms and their applications during the past decades. In this paper, we mainly provide a comprehensive review on ELM theoretical research and its variants in data stream classification, and categorize these algorithms from different perspectives. Firstly, we briefly introduce the basic principles of ELM and its characteristics. Secondly, we give an overview of different ELM variants to address the particular issues of data stream classification. Thirdly, we present an overview of different strategies to optimize the ELM, which have further improved the stability, accuracy and generalization ability of ELM, and briefly introduce some practical applications of ELM in data stream classification. Finally, we conduct several groups of experiments to compare the performance of ELM based models addressing the focused issues. Also, the open issues and prospects of ELM models used for stream classification are discussed, which are worthwhile to be further studied in the future.

Robust Extreme Learning Machine Using New Activation and Loss Functions Based on M-Estimation for Regression and Classification

This paper provides an analysis of the combining effect of novel activation function and loss function based on M-estimation in application to extreme learning machine (ELM), a feed-forward neural network. Due to the computational efficiency and classification/prediction accuracy of ELM and its variants, they have been widely exploited in the development of new technologies and applications. However, in real applications, the performance of classical ELMs deteriorates in the presence of outliers, thus, negatively impacting the precision and accuracy of the system. To further enhance the performance of ELM and its variants, we proposed novel activation functions based on the psi function of M and redescend the M-estimation method along with the smooth l 2-norm weight-loss functions to reduce the negative impact of the outliers. The proposed psi functions of several M and redescending M-estimation methods are more flexible to make more distinct features space. For the first time, the idea of the psi function as an activation function in the neural network is introduced in the literature to ensure accurate prediction. In addition, new robust l 2 norm-loss functions based on M and redescending M-estimation are proposed to deal with outliers efficiently in ELM. To evaluate the performance of the proposed methodology against other state-of-the-art techniques, experiments have been performed in diverse environments, which show promising improvements in application to regression and classification problems.

Optimization of extreme learning machine model with biological heuristic algorithms to estimate daily reference evapotranspiration in Hetao Irrigation District of China

ABSTRACT Due to frequent drought events, increased water demand for agricultural production and limited, accurate estimation of reference evapotranspiration (ETo) is necessary for developing crop irrigation schemes and rational allocation of regional water resources. The extreme learning machine (ELM) was optimized using four biological heuristic algorithms, namely, Grey Wolf Optimizer (GWO-ELM), Moth-Flame Optimization (MFO-ELM), Particle Swarm Optimization (PSO-ELM), Whale Optimization Algorithm (WOA-ELM), and besides three types of empirical models (temperature-, radiation-, and mass transfer-based), and Penman model (P-M) were also applied to estimate the daily ETo in the Hetao irrigation district (HID). The results demonstrated that GWO-ELM obtained the highest estimation accuracy (R2 = 0.945–0.955; RRMSE = 14.52–15.29%; MAE = 0.124–0.141 mm d−1, and NSE = 0.942–0.952) at all stations when using mass transfer combination (Tmax, Tmin, RH, u2) as models input, and the GWO-ELM hybrid model outperformed other models. Herein, the biogenic heuristic algorithm can effectively enhance the ELM performance in ETo estimation, it was strongly recommended for estimating daily ETo in the HID using the hybrid GWO-ELM model and mass transfer combination as input. The optimized hybrid algorithms, especially GWO-ELM, can accurately estimate daily ETo with limited meteorological data, which can provide scientific guidance for the development of precision agriculture in the HID. Abbreviations: ANN: artificial neural network; BP: back propagation neural network; CMA: China Meteorological Administration; D-T: Dalton; EL: elevation; ELM: extreme learning machine; ETo: reference evapotranspiration; GEP: gene expression programming; GP: genetic programming; GWO: gray wolf optimization; H-S: Hargreaves-Samani; MFO: moth flame optimization; ML: machine learning; P-M model: FAO-56 Penman-Monteith (P-M) model; P-T: Priestley-Taylor; PSO: particle swarm optimization; RF: random forest; R-O: Rohwer; SVM: support vector machine; SVR: support vector regression; WOA: whale optimization algorithm

Application of Extreme Learning Machine Algorithm for Drought Forecasting

Drought is a complex and frequently occurring natural hazard in many parts of the world. Therefore, accurate drought forecasting is essential to mitigate its adverse impacts. This research has inferred the implication and the appropriateness of the extreme learning machine (ELM) algorithm for drought forecasting. For numerical evaluation, time series data of the Standardized Precipitating Temperature Index (SPTI) are used for nine meteorological stations located in various climatological zones of Pakistan. To assess the performance of ELM, this research includes parallel inferences of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models. The performance of each model is assessed using root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), Kling-Gupta efficiency (KGE), Willmott index (WI), and Karl Pearson’s correlation coefficient. Generally, graphical results illustrated an excellent performance of the ELM algorithm over MLP and ARIMA models. For training data of SPTI-1, ELM’s best performance has observed at Chitral station (RMSE = 0.374, KGE = 0.838, WI = 0.960, MAE = 0.272, MAPE = 259.59, R = 0.93). For SPTI-1 at Astore station, the numerical results are (RMSE = 0.688, KGE = 0.988, WI = 0.997, MAE = 0.798, MAPE = 247.35). The overall results indicate that the ELM outperformed by producing the smallest RMSE, MAE, and MAPE values and maximum values for KGE, WI, and correlation coefficient values at almost all the selected meteorological stations for (1, 3, 6, 9, and 12) month time scales. In summary, this research endorses the use of ELM for accurate drought forecasting.

Coupled extreme learning machine and particle swarm optimization variant for projectile aerodynamic identification

Accurate aerodynamic parameters are the basis for the research of uncontrolled projectile drop point dispersion and precise strike. The traditional methods for aerodynamic parameters rely strongly on the projectile dynamics system. To weaken the influence of kinetic effects, an extreme learning machine (ELM) optimized by particle swarm optimization (PSO) is applied. However, the iterative optimization process of PSO makes the algorithm more complicated and impairs the real-time performance of ELM. To accelerate the convergence of PSO, a new hybrid optimization strategy is proposed. The hybrid optimization strategy combines the advantages of the chaos optimization strategy, the adaptive update strategy, and the mutation strategy. The chaos optimization strategy optimizes the distribution of the initial swarm to improve the optimization efficiency of PSO. The adaptive update strategy tunes the velocity inertia weight based on the value of the evolutionary factor to match the current searching state of the particles. The mutation strategy mutates the particles to break out of the local convergence. Numerical experiments show that the introduction of the hybrid optimization strategy enables ELM to exhibit excellent robustness, real-time performance, and accuracy in noisy environments. The hybrid algorithm has excellent prospects for application and extension for the parameter identification of dynamic systems in complex environments.

A novel MCDM ensemble approach of designing an ELM based predictor for stock index price forecasting

In support of its faster learning capacity and better generalization, Extreme Learning Machine (ELM) has gained the attention of researchers as a means to solve various real-world prediction problems. However, the performance of ELM is heavily dependent on the activation functions used in it. In this study, design of ELM is addressed as a Multi Criteria Decision Making (MCDM) problem. The selection of the activation function for the ELM based predictor model is done through a novel MCDM ensemble approach. On the basis of 9 prediction metrics, MCDM techniques such as TOPSIS, PROMETHEE-II, and VIKOR were used to assess and rank 15 activation functions on ELM performance. In light of the fact that the ranks determined by each MCDM technique do not coincide, a novel ensemble approach was proposed to calculate the final rank score by considering the occurrences of each model in the primary ranking and its respective rank score. In the end, the most highly ranked activation function is taken into account in the ELM-based predictor model. The proposed model is assessed over three benchmark stock indices such as BSE SENSEX, NIFTY 50 and BSE S&amp;P 500. The empirical analysis clearly shows that the ELM based predictor model designed using ELU activation function performs competitively compared to other reported models.

Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution

Birds are a kind of environmental indicator organism, which can reflect the changes in the ecological environment and biodiversity, and recognition of birdsongs can further help understand and protect birds and natural environment. Extreme learning machine (ELM) has the advantages of fast learning speed and good generalization ability, which is widely used in classification and recognition problems. Input layer weights and hidden layer thresholds are two key factors affecting ELM performance. As one of swarm intelligence optimization methods, differential evolution (DE) can be used to optimize the parameters of ELM. In order to enhance the diversity, convergence speed and global search ability of the DE population, and improve the accuracy and stability of the classification model, this paper proposes a multi-strategy differential evolution method (M-SDE) to optimize the parameters of the ELM. And the differential MFCC feature parameters, extracted from birdsongs, are applied to build classification models of M-SDE_ELM and an ensemble M-SDE_EnELM with optimized ELM for bird species recognition. In the experiments, the ELM models optimized by the swarm intelligence algorithms PSO and GOA are compared and analyzed by hypothesis tests with the M-SDE_ELM and M-SDE_EnELM. Results show that the M-SDE_ELM and M-SDE_EnELM can achieve a classification accuracy of 86.70% and 89.05% in the classification of nine species of birds respectively, and the recognition effect and stability of the M-SDE_EnELM model outperform other models.

Extreme learning machine model for state-of-charge estimation of lithium-ion battery using salp swarm algorithm

Accurate capacity estimation of lithium-ion batteries can effectively avoid over charge/discharge and improve life span. This paper proposes a state-of-charge (SOC) estimation model based on an improved extreme learning machine (ELM). ELM is a single hidden layer network with fast learning speed and good generalization performance, suitable for SOC estimation. For the ELM performance is highly dependent on network weights and hidden layer biases, a salp swarm algorithm (SSA) is applied to search for these parameters. Chaotic mapping is introduced in SSA to make the initialized individuals uniformly distributed. And a sine cosine algorithm (SCA) is embedded in swarm position formulation to promote communication. The model robustness and computational cost are verified by energy storage devices. The mean absolute error is 0.538, and the mean absolute percentage error is 0.887%. The results verified that the proposed model has better performance than other popular models, and shows good traceability and generality.

Fractal adaptive weight synthesized-local directional pattern-based image classification using enhanced tree seed algorithm.

Coral reefs are one of the most prominent marine ecosystems on the Earth; they are threatened due to various factors, including growing anthropogenic impacts and the effects of global change. Hence, the automatic classification of coral species is significant for tracking and detecting threatened and susceptible coral species. This paper proposes a new feature descriptor known as fractal adaptive weight synthesized-local directional pattern (FAWS-LDP) method to classify coral images using enhanced tree seed algorithm with extreme learning machine (ETSA-ELM) technique. The proposed feature descriptor (FAWS-LDP) inherits the advantage of both fractal pixel intensity information and local directional characteristics by indexing both feature vector values. Finally, the extracted features are imported to the extreme learning machine (ELM) network for classification. The ELM classifier is a single hidden layer feed-forward neural network with a faster learning speed and produces good generalization performance. The random selection of input weight and biases of the ELM classifier produces non-optimal or unnecessary input biases and weights to the network. Hence, to fine-tune the parameters of the ELM classifier, an enhanced tree seed algorithm (ETSA) is proposed. The proposed ETSA is a new learning technique to overcome the drawbacks like local optima and a lower coverage rate. The classification performance of ELM employing the ETSA optimizer is compared to the original TSA and other well-known metaheuristic algorithm (MHA) trainers, such as genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC), utilizing model performance measures like specificity, sensitivity, and classification accuracy. The evaluation of coral classification datasets reveals that the proposed ETSA-ELM produces consistently superior performance to existing methods. Finally, the proposed feature descriptor technique is statistically analyzed using a non-parametric Friedman test to demonstrate the efficiency.

A COMPARATIVE STUDY OF SEVERAL SLFN-BASED CLASSIFICATION ALGORITHMS FOR URBAN AND RURAL LAND USE

Abstract. In the study of urban sustainable development, accurate classification of land use has become an important basis for monitoring urban dynamic changes. Hence it is necessary to develop the appropriate recognition model for urban-rural land use. Although deep learning algorithms have become a research hotspot in image classification tasks in recent years, and many good results have been achieved. But other machine learning algorithms are not going away. Compared deep learning with machine learning, there are some advantages and disadvantages in data dependence, hardware dependence, feature processing, problem solving methods, execution time, and interpretability, etc. Especially in the classification for remote sensing images, the continuous research and development of traditional machine learning algorithms is still of great significance. In this paper, the performances of several SLFN-based classification algorithms were studied and compared, including ELM, RBF K-ELM, mixed K-ELM, A-ELM and SVM. Extreme Learning Machine (ELM) is a new algorithm for single-hidden-layer feedforward neural network (SLFN). It has simple structure, fast speed and is easy to train. In some applications, however, standard ELM is prone to be overfitting and its performance will be affected seriously when outliers exist. In order to explore the performance of ELM and its improved algorithm for urban-rural land use classification, comparative experiments between three improved ELM algorithms (RBF K-ELM, mixed K-ELM and A-ELM), ELM and SVM with image data from several study areas were performed, and the classification accuracy and efficiency were analysed. The results show that the three improved ELM algorithms perform better than the standard ELM and SVM both in overall accuracy and Kappa coefficient. However, it is worth noting that the computation efficiency of RBF K-ELM and mixed K-ELM decreases greatly with larger image, the time cost is much more than other algorithms. Compared with other algorithms, A-ELM has the advantages of higher Overall Accuracy and less classification time.