Machine learning model based on non-convex penalized huberized-SVM

The support vector machine(SVM)is a classical machine learning method.Both the hinge loss and least absolute shrinkage and selection operator(LASSO)penalty are usually used in traditional SVMs.However,the hinge loss is not differentiable,and the LASSO penalty does not have the Oracle property.In this paper,the huberized loss is combined with non-convex penalties to obtain a model that has the advantages of both the computational simplicity and the Oracle property,contributing to higher accuracy than traditional SVMs.It is experimentally demonstrated that the two non-convex huberized-SVM methods,smoothly clipped absolute deviation huberized-SVM(SCAD-HSVM)and minimax concave penalty huberized-SVM(MCP-HSVM),outperform the traditional SVM method in terms of the prediction accuracy and classifier performance.They are also superior in terms of variable selection,especially when there is a high linear correlation between the variables.When they are applied to the prediction of listed companies,the variables that can affect and predict financial distress are accurately filtered out.Among all the indicators,the indicators per share have the greatest influence while those of solvency have the weakest influence.Listed companies can assess the financial situation with the indicators screened by our algorithm and make an early warning of their possible financial distress in advance with higher precision.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents

BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity(FC).AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study.Using resting-state functional magnetic resonance imaging,the FC was compared between the adolescents with MDD and the healthy controls,with the bilateral amygdala serving as the seed point,followed by statistical analysis of the results.The support vector machine(SVM)method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.RESULTS Compared to the controls and using the bilateral amygdala as the region of interest,patients with MDD showed significantly lower FC values in the left inferior temporal gyrus,bilateral calcarine,right lingual gyrus,and left superior occipital gyrus.However,there was an increase in the FC value in Vermis-10.The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls,achieving a diagnostic accuracy of 83.91%,sensitivity of 79.55%,specificity of 88.37%,and an area under the curve of 67.65%.CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.

Learning Vector Quantization-Based Fuzzy Rules Oversampling Method

Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.

Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.

Lithofacies identi cation using support vector machine based on local deep multi-kernel learning

Lithofacies identification is a crucial work in reservoir characterization and modeling.The vast inter-well area can be supplemented by facies identification of seismic data.However,the relationship between lithofacies and seismic information that is affected by many factors is complicated.Machine learning has received extensive attention in recent years,among which support vector machine(SVM) is a potential method for lithofacies classification.Lithofacies classification involves identifying various types of lithofacies and is generally a nonlinear problem,which needs to be solved by means of the kernel function.Multi-kernel learning SVM is one of the main tools for solving the nonlinear problem about multi-classification.However,it is very difficult to determine the kernel function and the parameters,which is restricted by human factors.Besides,its computational efficiency is low.A lithofacies classification method based on local deep multi-kernel learning support vector machine(LDMKL-SVM) that can consider low-dimensional global features and high-dimensional local features is developed.The method can automatically learn parameters of kernel function and SVM to build a relationship between lithofacies and seismic elastic information.The calculation speed will be expedited at no cost with respect to discriminant accuracy for multi-class lithofacies identification.Both the model data test results and the field data application results certify advantages of the method.This contribution offers an effective method for lithofacies recognition and reservoir prediction by using SVM.

Support Vector Machine active learning for 3D model retrieval

In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user＇s semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.

Incremental support vector machine algorithm based on multi-kernel learning

A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.

A Hierarchical Clustering and Fixed-Layer Local Learning Based Support Vector Machine Algorithm for Large Scale Classification Problems

It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed- layer local learning （HCFLL） based support vector machine（SVM） algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically dusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision.tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models

Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introduced machine learning algorithms to path loss predictions because it offers a flexible network architecture and extensive data can be used. We introduced support vector regression (SVR) and radial basis function (RBF) models to path loss predictions in the investigated environments. The SVR model was able to process several input parameters without introducing complexity to the network architecture. The RBF on its part provides a good function approximation. Hyperparameter tuning of the machine learning models was carried out in order to achieve optimal results. The performances of the SVR and RBF models were compared and result validated using the root-mean squared error (RMSE). The two machine learning algorithms were also compared with the Cost-231, SUI, Egli, Freespace, Cost-231 W-I models. The analytical models overpredicted path loss. Overall, the machine learning models predicted path loss with greater accuracy than the empirical models. The SVR model performed best across all the indices with RMSE values of 1.378 dB, 1.4523 dB, 2.1568 dB in rural, suburban and urban settings respectively and should therefore be adopted for signal propagation in the investigated environments and beyond.

Facial Expression Recognition Model Depending on Optimized Support Vector Machine

In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.

POSITIVE DEFINITE KERNEL IN SUPPORT VECTOR MACHINE(SVM)

The relationship among Mercer kernel, reproducing kernel and positive definite kernel in support vector machine （SVM） is proved and their roles in SVM are discussed. The quadratic form of the kernel matrix is used to confirm the positive definiteness and their construction. Based on the Bochner theorem, some translation invariant kernels are checked in their Fourier domain. Some rotation invariant radial kernels are inspected according to the Schoenberg theorem. Finally, the construction of discrete scaling and wavelet kernels, the kernel selection and the kernel parameter learning are discussed.

Inverse Learning Control of Nonlinear Systems Using Support Vector Machines

An inverse learning control scheme using the support vector machine (SVM) for regression was proposed. The inverse learning approach is originally researched in the neural networks. Compared with neural networks, SVMs overcome the problems of local minimum and curse of dimensionality. Additionally, the good generalization performance of SVMs increases the robustness of control system. The method of designing SVM inverse learning controller was presented. The proposed method is demonstrated on tracking problems and the performance is satisfactory.

Learning control of nonhonolomic robot based on support vector machine

A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic controller based on SVM.The kinematic controller is aimed to provide desired velocity which can make the steering system stable.The dynamic controller is aimed to transform the desired velocity to control torque.The parameters of the dynamic system of the robot are estimated through SVM learning algorithm according to the training data of sliding windows in real time.The proposed controller can adapt to the changes in the robot model and uncertainties in the environment.Compared with artificial neural network(ANN)controller,SVM controller can converge to the reference trajectory more quickly and the tracking error is smaller.The simulation results verify the effectiveness of the method proposed.

Total organic carbon content logging prediction based on machine learning:A brief review

The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.

Research on Application of Support Vector Machine in Machine Learning

In recent years,support vector machine learning methods have gradually become the main research direction of machine learning.The support vector machine has a small structural risk compared with the traditional learning method,which can make the training error and the classifier capacity reach a relatively balanced state.Secondly,it also has the advantages of strong adaptability and strong promotion ability and has been widely praised by the industry.The following discussion focuses on the application of support vector machine in machine learning.

Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm

Classical machine learning, which is at the intersection of artificial intelligence and statistics, investigates and formulates algorithms which can be used to discover patterns in the given data and also make some forecasts based on the given data. Classical machine learning has its quantum part, which is known as quantum machine learning (QML). QML, which is a field of quantum computing, uses some of the quantum mechanical principles and concepts which include superposition, entanglement and quantum adiabatic theorem to assess the data and make some forecasts based on the data. At the present moment, research in QML has taken two main approaches. The first approach involves implementing the computationally expensive subroutines of classical machine learning algorithms on a quantum computer. The second approach concerns using classical machine learning algorithms on a quantum information, to speed up performance of the algorithms. The work presented in this manuscript proposes a quantum support vector algorithm that can be used to forecast solar irradiation. The novelty of this work is in using quantum mechanical principles for application in machine learning. Python programming language was used to simulate the performance of the proposed algorithm on a classical computer. Simulation results that were obtained show the usefulness of this algorithm for predicting solar irradiation.