Single Support Vector Machine Model Research Articles

Bayes' Theorem for Multi-Bearing Faults Diagnosis

During the process of fault diagnosis for automated machinery, support vector machines is one of the suitable choices to categorize multiple faults for machinery. Regardless of the volume of sampling data, support vector machines can handle a high number of input features. It was learned that support vector machines could only sense binary fault classification (such as faulty or healthy). However, the classification accuracy was found to be lower when using support vector machines to diagnose multi-bearing faults classifications. This is because the multiple classification problem will be reduced into several sub-problems of binary classification when support vector machines adapt to multi-bearing faults classifications. From there, many contradictory results will occur from every support vector machine model. In order to solve the situation, the combination of Support Vector Machines and Bayes’ Theorem is introduced to every single support vector machine model to overcome the conflicting results. This method will also increase classification accuracy. The proposed Support Vector Machines - Bayes’ Theorem method has resulted in an increase in the accuracy of the fault diagnosis model. The analysis result has shown an accuracy from 72% to 95%. It proved that Support Vector Machines - Bayes’ Theorem continuously eliminates and refines conflicting results from the original support vector machine model. Compared to the existing support vector machine, the proposed Support Vector Machines - Bayes’ Theorem has proven its effectiveness in diagnosing the multi-bearing faults problem classification.

A Support Vector Machine-Based Approach for Bolt Loosening Monitoring in Industrial Customized Vehicles.

Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with a high degree of customization of equipment and machines. Structures such as bolted joints are a key part of the industry; therefore, monitoring their health is critical to maintaining structural integrity. Despite this, there has been little research on the detection of bolt loosening in rotating joints. In this study, vibration-based detection of bolt loosening in a rotating joint of a custom sewer cleaning vehicle transmission was performed using support vector machines (SVM). Different failures were analyzed for various vehicle operating conditions. Several classifiers were trained to evaluate the influence of the number and location of accelerometers used and to determine the best approach between specific models for each operating condition or a single model for all cases. The results showed that using a single SVM model with data from four accelerometers mounted both upstream and downstream of the bolted joint resulted in more reliable fault detection, with an overall accuracy of 92.4%.

Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals.

Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with single-channel EEG signals for practical applications. The original sleep recordings were collected from the Sleep-EDF database. The wavelet threshold denoising (WTD) method and wavelet packet transformation (WPT) method were applied as signal preprocessing to extract six kinds of characteristic waves. With a comprehensive feature system including time, frequency, and nonlinear dynamics, we obtained the sleep stage classification results with different Support Vector Machine (SVM) models. We proposed a novel classification method based on cascaded SVM models with various features extracted from denoised EEG signals. To enhance the accuracy and generalization performance of this method, nonlinear dynamics features were taken into consideration. With nonlinear dynamics features included, the average classification accuracy was up to 88.11% using this method. In addition, with cascaded SVM models, the classification accuracy of the non-rapid eye movement sleep stage 1 (N1) was enhanced from 41.5% to 55.65% compared with the single SVM model, and the overall classification time for each epoch was less than 1.7 s. Moreover, we demonstrated that it was possible to apply this method for long-term sleep stage monitor applications.

Credit Risk Evaluation of Supply Chain Finance Based on K-Means-SVM Model

With the rapid development of supply chain finance, it is important to evaluate its credit risk effectively. The Support Vector Machine (SVM) is designed to construct the credit risk measurement model of supply chain finance. Considering the characteristics of SVM model, we select the clustering center based on K-Means clustering algorithm and the edge points far from the clustering center as training samples to train the SVM model. Experimental results show that compared with single SVM model, the overall classification accuracy of K-means-SVM model is increased by 7.2%, and the first type error rate is reduced by 5.0%, which verifies the superiority and effectiveness of k-means-SVM model applied to enterprise credit risk assessment under supply chain finance mode.

Credit Risk Evaluation of Supply Chain Finance Based on K-Means-SVM Model

With the rapid development of supply chain finance, it is important to evaluate its credit risk effectively. The Support Vector Machine (SVM) is designed to construct the credit risk measurement model of supply chain finance. Considering the characteristics of SVM model, we select the clustering center based on K-Means clustering algorithm and the edge points far from the clustering center as training samples to train the SVM model. Experimental results show that compared with single SVM model, the overall classification accuracy of K-means-SVM model is increased by 7.2%, and the first type error rate is reduced by 5.0%, which verifies the superiority and effectiveness of k-means-SVM model applied to enterprise credit risk assessment under supply chain finance mode.

Development of hybrid machine learning-based carbonation models with weighting function

Carbonation of concrete has significant influence on the service life of constructions and great effort has been made to establish an accurate and efficient model of carbonation that incorporates both internal and external factors. We present a hybrid machine learning (ML) approach that combined two single ML models: artificial neural network (ANN) and support vector machine (SVM). A literature survey generated a database containing 532 records of accelerated carbonation depth measurements for concrete mixtures inclusive of fly-ash blends. Six inputs comprising cement content, fly-ash replacement level, water–binder ratio (w/b), CO2 concentration, relative humidity and exposure time were selected for modeling, justified by grey relational analysis. The four ML models had excellent accuracy in predicting the carbonation depth of concrete, with the correlation coefficient ranging from 0.9788 to 0.9946, but the two hybrid ML models achieved superior performance to the single ANN and SVM models with characteristic higher correlation coefficients, lower mean value for the absolute error and lower standard deviation for its distribution. In addition, compared with other commonly known empirical carbonation models, the hybrid ML models showed more accurate carbonation depth prediction with smaller root mean square error. Furthermore, the weightings of the contributions of six selected factors to carbonation depth disclosed that CO2 concentration, w/b and binder content had higher relative importance to carbonation depth and should be given greater weighting in future carbonation model development.

Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.

DeepCUBIT: Predicting Lymphovascular Invasion or Pathological Lymph Node Involvement of Clinical T1 Stage Non-Small Cell Lung Cancer on Chest CT Scan Using Deep Cubical Nodule Transfer Learning Algorithm.

The prediction of lymphovascular invasion (LVI) or pathological nodal involvement of tumor cells is critical for successful treatment in early stage non-small cell lung cancer (NSCLC). We developed and validated a Deep Cubical Nodule Transfer Learning Algorithm (DeepCUBIT) using transfer learning and 3D Convolutional Neural Network (CNN) to predict LVI or pathological nodal involvement on chest CT images. A total of 695 preoperative CT images of resected NSCLC with tumor size of less than or equal to 3 cm from 2008 to 2015 were used to train and validate the DeepCUBIT model using five-fold cross-validation method. We also used tumor size and consolidation to tumor ratio (C/T ratio) to build a support vector machine (SVM) classifier. Two-hundred and fifty-four out of 695 samples (36.5%) had LVI or nodal involvement. An integrated model (3D CNN + Tumor size + C/T ratio) showed sensitivity of 31.8%, specificity of 89.8%, accuracy of 76.4%, and AUC of 0.759 on external validation cohort. Three single SVM models, using 3D CNN (DeepCUBIT), tumor size or C/T ratio, showed AUCs of 0.717, 0.630 and 0.683, respectively on external validation cohort. DeepCUBIT showed the best single model compared to the models using only C/T ratio or tumor size. In addition, the DeepCUBIT model could significantly identify the prognosis of resected NSCLC patients even in stage I. DeepCUBIT using transfer learning and 3D CNN can accurately predict LVI or nodal involvement in cT1 size NSCLC on CT images. Thus, it can provide a more accurate selection of candidates who will benefit from limited surgery without increasing the risk of recurrence.

Theory and Application of Logistic-SVM Two-Stage Hybrid Model

Based on the theoretical analysis, this paper combined the Logistic model and the support vector machine (SVM) to construct a Logistic-SVM two-stage hybrid model for credit risk evaluation. Using the two-stage hybrid model to evaluate the credit risk of pharmaceutical supply chain finance, the results showed that the Logistic-SVM two-stage hybrid model had a higher evaluation level than the single Logistic model and the single SVM model, which verified the superiority and effectiveness of the Logistic-SVM two-stage hybrid model applied to the credit risk evaluation.

Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach

This work employs the application of three artificial intelligence (AI) techniques namely; support vector machine (SVM), Hammerstein-Wiener (HW) and multi-layer perceptron (MLP) for predicting the qualitative properties of an anti-Alzheimer agent using high-pressure liquid chromatography technique. The mobile phase (inform of acetonitrile and trifluoroacetic acid) and the column temperature was used as the predictors in modelling the maximum retention time (tR-max) and resolution (Resol.) as the output variables of the analyte. The measured and predicted values were checked using three performance indices including; Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC) as the goodness of fits and a statistical error inform of root-mean-square error (RMSE). The results obtained demonstrated the promising ability of AI-based models in modelling the qualitative properties of the anti-Alzheimer agent. Observation of different outputs of the AI-based models at various time intervals showed the necessity of ensembling the outputs of the AI-based models. Therefore, simple average ensemble and support vector machine ensemble (SVM-E) were employed to enhance the performance skills of the simple models. The comparative performance of SVM-E inform of NSE indicated its ability in boosting and enhancing the performance skills of the single models SVM, MLP and HW models up to 5, 13 and 20% respectively in the testing stage for modelling tR-max.

Study on Evaluation Model of Chinese P2P Online Lending Platform Based on Hybrid Kernel Support Vector Machine

Accurate evaluation of the risk level and operation performances of P2P online lending platforms is not only conducive to better functioning of information intermediaries but also effective protection of investors’ interests. This paper proposes a genetic algorithm (GA) improved hybrid kernel support vector machine (SVM) with an index system to construct such an evaluation model. A hybrid kernel consisting of polynomial function and radial basis function is improved, specifically kernel parameters and the weight of two kernels, by GA method with excellent global optimization and rapid convergence. Empirical testing based on cross-sectional data from Chinese P2P lending market demonstrates the superiority of the improved hybrid kernel SVM model. The classification accuracy of credit risk level and operation quality is higher than the single kernel SVM model as well as the hybrid kernel model with empirical parameter values.

Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

The aim of this study was twofold: (1) to assess the performance accuracy of support vector machine (SVM) models with different kernels to predict rock brittleness and (2) compare the inputs’ importance in different SVM models. To this end, the authors developed eight SVM models with different kernel types, i.e., the radial basis function (RBF), the linear (LIN), the sigmoid (SIG), and the polynomial (POL). Four of these models were developed using only the SVM method, while the four other models were hybridized with a feature selection (FS) technique. The performance of each model was assessed using five performance indices and a simple ranking system. The results of this study show that the SVM models developed using the RBF kernel achieved the highest ranking values among single and hybrid models. Concerning the importance of variables for predicting the brittleness index (BI), the Schmidt hammer rebound number (Rn) was identified as the most important variable by the three single-based models, developed by POL, SIG, and LIN kernels. However, the single SVM model developed by RBF identified density as the most important input variable. Concerning the hybrid SVM models, three models that were developed using the RBF, POL, and SIG kernels identified the point load strength index as the most important input, while the model developed using the LIN identified the Rn as the most important input. All four single-based SVM models identified the p-wave velocity (Vp) as the least important input. Concerning the least important factors for predicting the BI of the rock in hybrid-based models, Vp was identified as the least important factor by FS-SVM-POL, FS-SVM-SIG, and FS-SVM-LIN, while the FS-SVM-RBF identified Rn as the least important input.

Multi-classifier information fusion in risk analysis

This paper develops a novel multi-classifier information fusion approach that integrates the probabilistic support vector machine (SVM) and the improved Dempster-Shafer (D-S) evidence theory to support risk analysis under uncertainty. Safety levels for various risk factors can be classified separately using the probabilistic SVM. Then, these multiple classification results will be fused at the decision level to achieve an overall risk evaluation by an improved d-S evidence theory with the integration of the Dempster’ rule and the weighted average rule. The Monte Carlo simulation approach is employed to model the randomness and uncertainty underlying limited observations. A global sensitivity analysis is performed to identify the most significant factors contributing to the risk event. A realistic operational tunnel case in China is used to demonstrate the feasibility and effectiveness of the developed approach, aiming to assess the magnitude of the structural health risk. Results indicate the developed SVM-DS approach is capable of (1) Fusing multi-classifier information effectively from different SVM models with a high classification accuracy of 97.14%; (2) Performing a strong robustness to bias, which can achieve acceptable classification accuracy even under a 20% bias; and (3) Exhibiting a more outstanding classification performance (87.99% accuracy) than the single SVM model (63.84% accuracy) under a high bias (20%). Since the proposed reliable risk analysis method can efficiently fuse multi-sensory information with ubiquitous uncertainties, conflicts, and bias, it provides in-depth analysis for structural health status together with the most critical risk factors, and then proper remedial actions can be taken at an early stage.

Forecasting ASEAN Tourist Arrivals in Malaysia using Different Time Series Models

In this study three time series models are used for forecasting monthly ASEAN tourist arrivals in Malaysia from January 1999 to December 2015. Brunei, Thailand and Vietnam of ASEAN country selected as case study. This paper compares the forecasting accuracy of seasonal autoregressive integrated moving average (SARIMA), Support Vector Machine (SVM) and Wavelet Support Vector Machine (WSVM) and Empirical Mode Decomposition with Wavelet Support Vector Machine (EMD_WSVM) using root mean square error (RMSE) and mean absolute percentage error (MAPE) criterion. Moreover, correlation test has also been carried out to strengthen decisions, and to check accuracy of various forecasting models. Based on the forecasting performance of all four models, hybrid model SARIMA and EMD_WSVM are found to be best models as compare to single model SVM and hybrid model WSVM.

Identifying orbital angular momentum modes in turbulence with high accuracy via machine learning

The orbital angular momentum (OAM) of a Laguerre–Gaussian beam (LGB) is classified using the support vector machine (SVM) model. The scintillation index, beam width, and beam wander of the Gaussian beam and the LGB at the receiving site are taken as feature vectors, and the OAM number of a LGB is taken as the label in the training and test samples. The influences on the detection accuracy of the number of training samples, the transmission distance, and the different ways of grouping OAM values are analyzed, where the detection accuracy is defined as the percentage of correctly detected OAM. The results show that only a few training samples are needed up to a range of 4000 m to achieve saturation accuracy. When the transmission distance is within 2500 m, the detection accuracy of a single SVM model in four turbulent environments ( 10−14, 10−15, ) is close to 100%. When the distance increases, detection accuracy decreases gradually, and in the range of 4000 m the detection accuracy is more than 91%. Compared with the traditional spiral spectrum expansion method, the SVM model has obvious advantages. In addition, when the range of OAM to be detected is large, the OAM values may be grouped thereby improving the accuracy when using the multi-SVM models for joint detection.

Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers

Abstract A method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs) is presented in this work. The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets, local binary patterns (LBP), higher order statistics (HOS) and several amplitude values were employed. Instead of concatenating all these features to feed a single SVM model, we propose to train specific SVM models for each type of feature. In order to obtain the final prediction, the decisions of the different models are combined with the product, sum, and majority rules. The designed methodology approaches are tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal and normal beats. Our approach based on an ensemble of SVMs offered a satisfactory performance, improving the results when compared to a single SVM model using the same features. Additionally, our approach also showed better results in comparison with previous machine learning approaches of the state-of-the-art.

Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using Standardized Precipitation Index (case study: Urmia Lake, Iran)

Abstract In this study, wavelet-support vector machine (WSVM) is proposed for drought forecasting using the Standardized Precipitation Index (SPI). In this way, the SPI time series of Urmia Lake watershed is decomposed to multiple frequency time series by wavelet transform. Then, these time sub-series are applied as input data to the support vector machine (SVM) model to forecast drought. Also, a cuckoo search (CS)-based approach is proposed for parameter optimization of SVM, finding the best initial constant parameters of the SVM algorithm. The obtained results indicate that the radial basis function (RBF)-kernel function of the SVM algorithm has high efficiency in the SPI modeling, resulting in a determination coefficient (DC) of 0.865 in verification step. In the WSVM model, the Coif1, which is considered as a mother wavelet function with decomposition level of five, shows a better performance with DC of 0.954 in verification step, revealing that the proposed hybrid WSVM model outperforms the single SVM model in forecasting SPI time series. Also, DC of cuckoo search-support vector machine (CS-SVM) is calculated to be 0.912 in verification step, indicating the fact that the proposed CS-SVM model shows better efficiency than single SVM model.

A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

The main aim of this study is to propose a novel hybrid intelligent model named MBSVM which is an integration of the MultiBoost ensemble and a support vector machine (SVM) for modeling of susceptibility of landslides in the Uttarakhand State, Northern India. Firstly, a geospatial database for the study area was prepared, which includes 391 historical landslides and 16 landslide-affecting factors. Then, the sensitivity of different combinations of these factors for modeling was validated using the forward elimination technique. The MBSVM landslide model was built using the datasets generated from the best selected factors and validated utilizing the area under the receiver operating characteristic (ROC) curve (AUC), statistical indexes, and the Wilcoxon signed-rank test. Results show that this novel hybrid model has good performance both in terms of goodness of fit with the training dataset (AUC = 0.972) and the capability to predict landslides with the testing dataset (AUC = 0.966). The efficiency of the proposed model was then validated by comparison with logistic regression (LR), a single SVM, and another hybrid model of the AdaBoost ensemble and an SVM (ABSVM). Comparison results show that the MBSVM outperforms the LR, single SVM, and hybrid ABSVM models. Thus, the proposed model is a promising and good alternative tool for landslide hazard assessment in landslide-prone areas.

Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data

Garlic prices fluctuate dramatically in recent years and it is very difficult to predict garlic prices. The autoregressive integrated moving average (ARIMA) model is currently the most important method for predicting garlic prices. However, the ARIMA model can only predict the linear part of the garlic prices, and cannot predict its nonlinear part. Therefore, it is urgent to adopt a method to analyze the nonlinear characteristics of garlic prices. After comparing the advantages and disadvantages of several major prediction models which used to forecast nonlinear time series, using support vector machine (SVM) model to predict the nonlinear part of garlic prices and establish ARIMA-SVM hybrid forecast model to predict garlic prices. The monthly average price data of garlic in 2010-2017 was used to test the effect of ARIMA model, SVM model and ARIMA-SVM model. The experimental results show that: (1) Garlic price is affected by many factors but the most is the supply and demand relationship; (2) The SVM model has a good effect in dealing with the nonlinear relationship of garlic prices; (3) The ARIMA-SVM hybrid model is better than the single ARIMA model and SVM model on the accuracy of garlic price prediction, it can be used as an effective method to predict the short-term price of garlic.

A support vector machine-based ensemble algorithm for breast cancer diagnosis

This research studies a support vector machine (SVM)-based ensemble learning algorithm for breast cancer diagnosis. Illness diagnosis plays a critical role in designating treatment strategies, which are highly related to patient safety. Nowadays, numerous classification models in data mining domains are adapted to breast cancer diagnosis based on patients’ historical medical records. However, the performance of each algorithm depends on various model configurations, such as input feature types and model parameters. To tackle the limitation of individual model performance, this research focuses on breast cancer diagnosis that uses an SVM-based ensemble learning algorithm to reduce the diagnosis variance and increase diagnosis accuracy. Twelve different SVMs, based on the proposed Weighted Area Under the Receiver Operating Characteristic Curve Ensemble (WAUCE) approach, are hybridized. To evaluate the performance of the proposed model, Wisconsin Breast Cancer, Wisconsin Diagnostic Breast Cancer, and the U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program breast cancer datasets have been studied. The experimental results show that the WAUCE model achieves a higher accuracy with a significantly lower variance for breast cancer diagnosis compared to five other ensemble mechanisms and two common ensemble models, i.e., adaptive boosting and bagging classification tree. The proposed WAUCE model reduces the variance by 97.89% and increases accuracy by 33.34%, compared to the best single SVM model on the SEER dataset. In practice, the proposed methodology can be further applied to other illness diagnoses, which offers an alternative to a safer, more reliable, and more robust illness diagnosis process.