Kernel Support Vector Machine Model Research Articles

Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis.

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings.

Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning Algorithms

Aiming at solving the problem that it is difficult to recognize the quiet period of acoustic emission in rocks, four machine learning algorithms were adopted to develop and improve the recognition method of the quiet period of acoustic emission. In the process of establishing the model, the time domain data of acoustic emission were standardized and processed by box diagram method, so as to clean the abnormal data and reduce the dimension, and the frequency domain data were denoised by wavelet four-layer transform and wavelet packet three-layer energy decomposition, and a group of 8 wavelet packet energy parameters were established as frequency domain characteristic parameters. Based on AE time domain data, frequency domain data, and composite data (time-frequency domain data sets), the grid search traversal parameter technique was used to obtain the optimal parameters of four machine learning models. The accuracy, precision, recall, and F 1 score were used to verify and evaluate the recognition performance of the models. The study results show that the recognition effects of the models are good, the model accuracy of the frequency domain data set is the lowest, and the model accuracy of the composite data set is the highest, with an accuracy of more than 90%. The kernel support vector machine model has the best performance, and its average precision is 0.87. The random forest (RF) model is the best model for recognizing quiet period of acoustic emission.

Prediction Method of Beijing Electric-Energy Substitution Potential Based on a Grid-Search Support Vector Machine

Recently, “power cuts” and “coal price surges” have been significant concerns of individuals and societies. The main reasons for a power cut are a recent rapid increase in power consumption, shortage of thermal coal or the large shutdown capacity of thermal power units, resulting in a tight power supply in the power grid. In recent years, the shortage of fossil resources has led to frequent energy crises. In the context of carbon peaks and carbon neutralization, how to better develop electric-energy substitution and eliminate the dependence on fossil energy has become a problem that needs to be solved at present. In this paper, the influencing factors of electric-energy substitution in Beijing are analyzed, and the indexes affecting the electric-energy substitution are outlined. By constructing various machine-learning models, the prediction is performed. The results show that the Gaussian kernel support vector machine model based on a grid search has a good prediction effect on the electric-energy substitution potential in Beijing, which has certain guiding significance for electric-energy substitution potential analysis.

Validating Multivariate Classification Algorithms in Raman Spectroscopy-Based Osteosarcoma Cellular Analysis

Raman microspectroscopy has been widely demonstrated as an ideal analytical tool for preclinical drug development and clinical applications. However, it is still not easy to accurately identify the subtle spectral variations in different biological samples, which requires a feasible combination between novel spectra collection instrumentation and effective data mining algorithms. In this study, three distinct multivariate classification approaches, which were principal component analysis-linear discriminant analysis (PCA-LDA), support vector machine (SVM), and principal component analysis-support vector machine (PCA-SVM), were validated and compared for obtaining reliable and chemically significant results from the analysis of bio-spectral data. Their performances were evaluated by classifying the spectral characteristics of osteosarcoma cells treated with N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT) from untreated cells. Based on the discriminated spectral variations, the results indicate that PCA combined with the radial basis function (RBF) kernel SVM model achieved the highest classification accuracy. In general, this study confirms that PCA-SVM algorithm improves the automatic processing accuracy and efficiency of micro-Raman spectroscopy, which may be adopted in further cell screening and analysis applications.

Assessing black tea quality based on visible–near infrared spectra and kernel-based methods

Current evaluation systems for tea quality assessment are difficult to use and unstable. A rapid and cheap assessment method is required to distinguish the quality levels of tea. In this study, a visible-near-infrared (Vis–NIR) spectrometer and support vector machine (SVM)-based kernels were used for the qualitative categorization of black tea. First, a Vis–NIR system was used to acquire the spectral data of seven levels of tea samples. The obtained spectra were preprocessed using the Savitzky–Golay smoothing combined with the first derivative and standard normal variate transformation. Then, four characteristic wavelength selection algorithms, namely synergy interval partial least square, competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), and the interval VISSA, were used to obtain preprocessed spectral features. Finally, the SVM was used with four kernel functions, namely the linear, Gaussian, quadratic, and cubic functions, to develop models based on the variables obtained from the selected features for tea quality classification. The results revealed that the CARS–linear kernel SVM model exhibited the best results, with a correct identification rate of 91.85 % in the validation process. Our findings demonstrate that Vis-NIR spectroscopy can be a rapid, inexpensive, efficient, and alternative method for predicting the quality of black tea.

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.

High-Grade Serous Ovarian Cancer: Use of Machine Learning to Predict Abdominopelvic Recurrence on CT on the Basis of Serial Cancer Antigen 125 Levels

PurposeThe aim of this study was to use machine learning to predict abdominal recurrence on CT on the basis of serial cancer antigen 125 (CA125) levels in patients with advanced high-grade serous ovarian cancer on surveillance. MethodsThis institutional review board–approved, HIPAA-compliant, retrospective, hypothesis-generating study included all 57 patients (mean age, 61 ± 11.2 years) with advanced high-grade serous ovarian cancer who underwent cytoreductive surgery from January to December 2012, followed by surveillance abdominopelvic CT and corresponding CA125 levels. A blinded radiologist reviewed abdominopelvic CT studies until recurrence was noted. Four measures of CA125 were assessed: actual CA125 levels at the time of CT, absolute change since prior CT, relative change since prior CT, and rate of change since prior CT. Using machine learning, support vector machine models were optimized and evaluated using 10-fold cross-validation to determine the CA125 measure most predictive of abdominal recurrence. The association of the most accurate CA125 measure was further analyzed using Cox proportional-hazards model along with age, tumor size, stage, and degree of cytoreduction. ResultsRate of change in CA125 was most predictive of abdominal recurrence in a linear kernel support vector machine model and was significantly higher preceding CT studies showing abdominal recurrence (median 13.2 versus 0.6 units/month; P = .007). On multivariate analysis, a higher rate of CA125 increase was significantly associated with recurrence (hazard ratio, 1.02 per 10 units change; 95% confidence interval, 1.0006-1.04; P = .04). ConclusionA higher rate of CA125 increase is associated with abdominal recurrence. The rate of increase of CA125 may help in the selection of patients who are most likely to benefit from abdominopelvic CT in surveillance of ovarian cancer.

Preference risk assessment method based on grey multi index kernel support vector machine model

To improve the precision of sustainable development analysis of leisure agriculture in rural area, a method for the sustainable development analysis of leisure agriculture in rural area based on grey level multi-item kernel SVM model is proposed. Firstly, take such factors as the environmental characteristics, product characteristics, operation level and infrastructure construction of the leisure agriculture into full consideration to build the leisure agriculture evaluation index system and model; secondly, for the “failure” of extreme preference risk evaluation of SVM model, make use of the improved grey level model to preprocess the preference data record and take advantage of the multi-item kernel to improve the SVM algorithm to realize the improvement of the prediction performance of sample data auto-regression model; finally, verify the effectiveness of the method proposed through empirical analysis and give advice and suggestions on the strengthening of the sustainable development of leisure agriculture in rural area.

Dimension reduction based on a penalized kernel support vector machine model

Prediction and dimension reduction play increasingly significant roles in high-dimensional data analysis; however, they suffer from model inaccuracy, selection inconsistency and a prohibitively expensive computational cost as the model dimension increases exponentially. Although a support vector machine (SVM) is one of the most powerful forecasting approaches that are widely used by the research community, it does not provide an interpretable model that violates the principle of Occam’s razor. In classical regression problems, a penalized linear SVM model for the dimension reduction task has been investigated in the linear feature space under the assumption that the underlying true model is linear. In this paper, the penalized kernel SVM (PKSVM) model is proposed and investigated combining with a SVM information criterion (SVMIC) for the dimension reduction task in the nonlinear kernel space using radial basis function for prediction and model representation. Instead of pursuing sparsity in the original feature space of the SVM, dimension reduction is performed in the kernel feature space. Computationally, a fast and simple-to-implement algorithm is derived. Furthermore, both SVMIC and cross-validation are utilized to select the kernel and regularization parameters to guarantee the model consistency. Real data applications including microarray data analysis and global solar radiation forecasting are provided. The first application selected significant components in genetic study and the important data information is extracted for grid-connected photovoltaic installations in the second application. The proposed dimension reduction technique avoids the model redundancy which is of great importance for knowledge discovery.

Multimode Monitoring of Oxy-Gas Combustion Through Flame Imaging, Principal Component Analysis, and Kernel Support Vector Machine

ABSTRACTThis article presents a method for the multimode monitoring of combustion stability under different oxy-gas fired conditions based on flame imaging, principal component analysis (PCA), and kernel support vector machine (KSVM) techniques. The images of oxy-gas flames are segmented into premixed and diffused regions through the watershed transform method. The weighted color and texture features of the diffused and premixed regions are extracted and projected into two subspaces using the PCA to reduce the data dimensions and noises. The multi-class KSVM model is finally built based on the flame features in the principal component subspace to identify the operation condition. Two classic multivariate statistic indices, for example, Hotelling’s T2 and squared prediction error, are used to assess the normal and abnormal states for the corresponding operation condition. The experimental results obtained on a lab-scale oxy-gas rig show that the weighted color and texture features of the defined diffused and premixed regions are effective for detecting the combustion state and that the proposed PCA-KSVM model is feasible and effective to monitor a combustion process under variable operation conditions.

SN-SVM: a sparse nonparametric support vector machine classifier

This paper introduces a novel sparse nonparametric support vector machine classifier (SN-SVM) which combines data distribution information from two state-of-the-art kernel-based classifiers, namely, the kernel support vector machine (KSVM) and the kernel nonparametric discriminant (KND). The proposed model incorporates some near-global variations of the data provided by the KND and, hence, may be viewed as an extension to the KSVM. Similarly, since the support vectors improve the choice of \(\kappa \)-nearest neighbors (\(\kappa -NN\)’s), it can also serve as an extension to the KND. The proposed model is capable of dealing with both heteroscedastic and non-normal data while avoiding the small sample size problem. The model is a convex quadratic optimization problem with one global optimal solution, so it can be estimated easily and efficiently using numerical methods. It can also be reduced to the classical KSVM model and as such existing SVM programs can be used for easy implementation. Through the Bayesian interpretation with the help of a Gaussian prior, we show that our method provides a sparse solution by assigning non-zero weights to only a fraction of the total number of training samples. This sparsity can be used by existing sparse classification algorithms to obtain better computational efficiency. The experimental results on real-world datasets and face recognition applications show that the proposed SN-SVM model improves the classification accuracy over contemporary classifiers and also provides sparser solution than the KSVM.

A New KSVM + KFD Model for Improved Classification and Face Recognition

The Kernel Support Vector Machine (KSVM) is a powerful nonlinear classification methodology where, the Support Vectors (SVs) fully describe the decision surface by incorporating local information in the Kernel space. On the other hand, the Kernel Fisher Discriminant(KFD) is a non-linear classifier which has proven to be powerful and competitive to several state-of-the-art classifiers. This paper proposes a novel KSVM + KFD model which combines these two methods. This model can be viewed as an extension to the KSVM by incorporating 'global' characteristics of the data to estimate the decision boundary in the Kernel space. On the other hand, this new model could also be considered as an improvement to the KFD by incorporating the Support Vectors (local margin concept) into the KFD formulation. The KSVM + KFD model can be reduced to the classical KSVM model so that existing KSVM softwares can be used for easy implementation. An extensive comparison of the KSVM + KFD to the KFD, KSVM, Linear Discriminant Analysis (LDA), Linear Support Vector Machine (LSVM) and the combined LSVM and LDA, performed on real data sets, has shown the advantages of our proposed model. In particular, the experiments on face recognition have clearly shown the superiority of the KSVM + KFD over other methods.