Least Squares Twin Support Vector Machine (LSTSVM) is a binary classifier and the extension of it to multiclass is still an ongoing research issue. In this paper, we extended the formulation of ...binary LSTSVM classifier to multi-class by using the concepts such as “One-versus-All”, “One-versus-One”, “All-versus-One” and Directed Acyclic Graph (DAG). This paper performs a comparative analysis of these multi-classifiers in terms of their advantages, disadvantages and computational complexity. The performance of all the four proposed classifiers has been validated on twelve benchmark datasets by using predictive accuracy and training–testing time. All the proposed multi-classifiers have shown better performance as compared to the typical multi-classifiers based on ‘Support Vector Machine’ and ‘Twin Support Vector Machine’. Friedman’s statistic and Nemenyi post hoc tests are also used to test significance of predictive accuracy differences between classifiers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Aiming at the problem that the most existing fault diagnosis methods could not effectively recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy information ...entropy, improved particle swarm optimization algorithm and least squares support vector machines are introduced into the fault diagnosis to propose a novel intelligent diagnosis method, which is applied to diagnose the faults of the motor bearing in this paper. In the proposed method, the vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by using empirical mode decomposition method. The fuzzy information entropy values of IMFs are calculated to reveal the intrinsic characteristics of the vibration signal and considered as feature vectors. Then the diversity mutation strategy, neighborhood mutation strategy, learning factor strategy and inertia weight strategy for basic particle swarm optimization (PSO) algorithm are used to propose an improved PSO algorithm. The improved PSO algorithm is used to optimize the parameters of least squares support vector machines (LS-SVM) in order to construct an optimal LS-SVM classifier, which is used to classify the fault. Finally, the proposed fault diagnosis method is fully evaluated by experiments and comparative studies for motor bearing. The experiment results indicate that the fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal. The improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods in this paper and published in the literature. It provides a new method for fault diagnosis of rotating machinery.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of ...screening is non-contact, easy to apply, and can reduce the workload in testing centres as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19. The datasets used in this study include subjects from all six continents and contain both forced and natural coughs, indicating that the approach is widely applicable. The publicly available Coswara dataset contains 92 COVID-19 positive and 1079 healthy subjects, while the second smaller dataset was collected mostly in South Africa and contains 18 COVID-19 positive and 26 COVID-19 negative subjects who have undergone a SARS-CoV laboratory test. Both datasets indicate that COVID-19 positive coughs are 15%–20% shorter than non-COVID coughs. Dataset skew was addressed by applying the synthetic minority oversampling technique (SMOTE). A leave-p-out cross-validation scheme was used to train and evaluate seven machine learning classifiers: logistic regression (LR), k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM) and a residual-based neural network architecture (Resnet50). Our results show that although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98. An LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs, with an AUC of 0.94 after selecting the best 13 features from a sequential forward selection (SFS). Since this type of cough audio classification is cost-effective and easy to deploy, it is potentially a useful and viable means of non-contact COVID-19 screening.
•A machine learning based COVID-19 cough classifier has been developed.•This classifier achieves the highest AUC of 0.98 from a residual based architecture.•Cough audio recordings are collected from all six continents of the globe.•COVID-19 positive coughs are 15% to 20% shorter than non-COVID coughs.•A special feature extraction technique preserves end-to-end time-domain patterns.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A ...novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.
A framework for semisupervised remote sensing image classification based on neural networks is presented. The methodology consists of adding a flexible embedding regularizer to the loss function used ...for training neural networks. Training is done using stochastic gradient descent with additional balancing constraints to avoid falling into local minima. The method constitutes a generalization of both supervised and unsupervised methods and can handle millions of unlabeled samples. Therefore, the proposed approach gives rise to an operational classifier, as opposed to previously presented transductive or Laplacian support vector machines (TSVM or LapSVM, respectively). The proposed methodology constitutes a general framework for building computationally efficient semisupervised methods. The method is compared with LapSVM and TSVM in semisupervised scenarios, to SVM in supervised settings, and to online and batch k -means for unsupervised learning. Results demonstrate the improved classification accuracy and scalability of this approach on several hyperspectral image classification problems.
Higher heating value (HHV) is an important parameter for design and operation of biomass-fueled energy systems. Experimental approach is always time-consuming and expensive for determinating this ...property compared with mathematical models. In this paper, three machine learning approaches, including artificial neural network (ANN), support vector machine (SVM) and random forest (RF), are employed for accurately estimating biomass HHV from ultimate or proximate analysis. The linear and nonlinear empirical correlations are also carried out for comparison. The results show machine learning approaches give better predictions (R2 > 0.90) compared with those of empirical correlations (R2 < 0.70), especially for the extreme values. The RF model shows the best performances for both the ultimate and proximate analysis, with the determination coefficient R2>0.94. The SVM and ANN approaches show similar performances with R2∼ 0.90. Ultimate-based models show better performances compared with those of the proximate-based models even with much less samples. Relative importance analysis shows for the proximate analysis, the ash, volatile matter and fixed carbon fractions show the maximum, medium and minimum effects, respectively. For the ultimate analysis, carbon and hydrogen fractions hold the first two significant places with carbon fraction having the most significant influence, while the oxygen and nitrogen fractions have limited effects.
•Estimating biomass HHV from biomass property via machine learning approaches.•Machine learning models give better predictions compared with empirical correlations.•Random forest model shows the best performance with R2 > 0.94.•Artificial neural network and support vector machine models show similar predictions.•Relative importance of each input on biomass HHV is explored.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A hybrid model is developed for short-term wind power prediction.•The model is based on LSSVM and gravitational search algorithm.•Gravitational search algorithm is used to optimize parameters of ...LSSVM.•Effect of different kernel function of LSSVM on wind power prediction is discussed.•Comparative studies show that prediction accuracy of wind power is improved.
Wind power forecasting can improve the economical and technical integration of wind energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to forecast wind power accurately. For the purpose of utilizing wind power to the utmost extent, it is very important to make an accurate prediction of the output power of a wind farm under the premise of guaranteeing the security and the stability of the operation of the power system. In this paper, a hybrid model (LSSVM–GSA) based on the least squares support vector machine (LSSVM) and gravitational search algorithm (GSA) is proposed to forecast the short-term wind power. As the kernel function and the related parameters of the LSSVM have a great influence on the performance of the prediction model, the paper establishes LSSVM model based on different kernel functions for short-term wind power prediction. And then an optimal kernel function is determined and the parameters of the LSSVM model are optimized by using GSA. Compared with the Back Propagation (BP) neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction. Therefore, the proposed LSSVM–GSA is a better model for short-term wind power prediction.
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IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Structured support vector machine (SSVM) is an effective method on coping with problems involving complex outputs such as multiple dependent output variables and structured output spaces. However, ...its training process is very time consuming for large-scale data with complex structure and many classes. In this paper, to improve the efficiency of SSVM, we propose a multiple structured support vector machine (MSSVM) for structured output classification via the idea of splitting large into small. By constructing novel classification loss for each class, MSSVM solves a series of smaller optimization problems rather than one large-size optimization problem in SSVM. Therefore, MSSVM greatly reduces the training speed of SSVM. In addition, the structured output label information and discriminative information are embedded in the introduced losses in a simple but effective way. Experiments on multiclass classification, ordinal regression and hierarchical classification datasets demonstrate the efficiency and effectiveness of the proposed MSSVM.
•This study introduces the use of the clustered support vector machine (CSVM) for credit scoring.•The CSVM has been shown to relax size constraints while remaining highly accurate.•The results ...suggest that the CSVM is a useful alternative to kernel SVM approaches when training datasets get large.
This work investigates the practice of credit scoring and introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations noted in the literature that is associated with traditional nonlinear support vector machine (SVM) based methods for classification. Specifically, it is well known that as historical credit scoring datasets get large, these nonlinear approaches while highly accurate become computationally expensive. Accordingly, this study compares the CSVM with other nonlinear SVM based techniques and shows that the CSVM can achieve comparable levels of classification performance while remaining relatively cheap computationally.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to ...detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti-noise ability and detect incipient fault, a novel fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis.
•An impact time-frequency dictionary is constructed based on mechanism of bearings.•A short-time matching method is proposed for impact signal characters extraction.•The oscillatory characters were used as inputs of support vector machine.•Simulations and experiments demonstrate the effectiveness of the method.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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