Smooth twin bounded support vector machine with pinball loss

Li, Kai; Lv, Zhen

doi:10.1007/s10489-020-02085-5

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…SVM is a common classification method suitable for solving small sample and non-linear problems (Huang et al, 2021;Li and Lv, 2021;Gupta et al, 2022). Based on the principle of structural risk minimization, SVM compresses the original data set into the support vector set, gives the rules of support vector determination in the process of learning new knowledge with subsets, and obtains the upper bound of learning error probability (Ahmad et al, 2021).…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Development of hybrid SVM-FA, DT-FA and MLR-FA models to predict the flexural strength (FS) of recycled concrete

Wang

Zhou

2023

Front. Mater.

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Recycled concrete from construction waste used as road material is a current sustainable approach. To provide feasible suggestions for civil engineers to prepare recycled concrete with high flexural strength (FS) for the road pavement, the present study proposed three hybrid machine learning models by combining support vector machine (SVM), decision tree (DT) and multiple linear regression (MLR) with the firefly algorithm (FA) for the computational optimization, named as SVM-FA, DT-FA, and MLR-FA, respectively. Effective water-cement ratio (WC), aggregate-cement ratio (AC), recycled concrete aggregate replacement ratio (RCA), nominal maximum recycled concrete aggregate size (NMR), nominal maximum normal aggregate size (NMN), bulk density of recycled concrete aggregate (BDR), bulk density of normal aggregate (BDN), water absorption of RCA (WAR) and water absorption of NA (WAN) were employed as the input variables. To determine the predicting results of varying hybrid models, root mean square error (RMSE) and correlation coefficient (R) were used as performance indexes. The results showed that the SVM-FA demonstrated the highest R values and the lowest RMSE values, and the fitting effect of the predicted values and the actual values of the FS of recycled concrete is the best. All the above analysis proving that the SVM optimized by FA hyperparameters has the highest prediction accuracy and SVM-FA can provide engineers a more accurate and convenient tool to evaluate the FS of recycled concrete. The results of sensitivity analysis showed that WC has the most significant influence on the FS of recycled concrete, while RCA has the weakest influence on the FS, which should be noticed when engineers apply recycled concrete to road design in the future.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Development of hybrid SVM-FA, DT-FA and MLR-FA models to predict the flexural strength (FS) of recycled concrete

Wang

Zhou

2023

Front. Mater.

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“…For the sake of scalability and efciency, we apply a stochastic gradient descent algorithm, i.e., Pegasos [43], to solve problem (19).…”

Section: Algorithm For Caensvmmentioning

confidence: 99%

“…m be a subset of k samples, randomly chosen from the whole dataset for the t-th iteration during optimizing a problem (19). Tus, we consider the following approximate objective function:…”

Section: Algorithm For Caensvmmentioning

confidence: 99%

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Capped Asymmetric Elastic Net Support Vector Machine for Robust Binary Classification

Yang

2023

International Journal of Intelligent Systems

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Recently, there are lots of literature on improving the robustness of SVM by constructing nonconvex functions, but they seldom theoretically study the robust property of the constructed functions. In this paper, based on our recent work, we present a novel capped asymmetric elastic net (CaEN) loss and equip it with the SVM as CaENSVM. We derive the influence function of the estimators of the CaENSVM to theoretically explain the robustness of the proposed method. Our results can be easily extended to other similar nonconvex loss functions. We further show that the influence function of the CaENSVM is bounded, so that the robustness of the CaENSVM can be theoretically explained. Other theoretical analysis demonstrates that the CaENSVM satisfies the Bayes rule and the corresponding generalization error bound based on Rademacher complexity guarantees its good generalization capability. Since CaEN loss is concave, we implement an efficient DC procedure based on the stochastic gradient descent algorithm (Pegasos) to solve the optimization problem. A host of experiments are conducted to verify the effectiveness of our proposed CaENSVM model.

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“…Whether it is a regression task or a classification task, it can be achieved by the support vector machine (SVM) method. is study intends to use the SVM algorithm to evaluate the quality of the observation data [12,13]. e SVM algorithm has obvious advantages in solving small sample data and high-dimensional data, and it can map the relationship between input and output data well.…”

Section: Introductionmentioning

confidence: 99%

Application of a New Loss Function-Based Support Vector Machine Algorithm in Quality Control of Measurement Observation Data

Tao

2022

Mathematical Problems in Engineering

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The loss function of the traditional support vector machine (SVM) method consists of hinge function and regularization, which is difficult to achieve the quality control of observation data. It requires a new loss function to measure the quality of the observed data. At this stage, researchers will use data cleaning or data preprocessing to process observational data. The data preprocessing method will normalize the data features, which will make the data process the same interval and a distribution. However, this method is also difficult to guarantee the accuracy of the data. This study uses the SVM method to study the integrity, unity, and accuracy of the observation data. This study also proposes a new loss function, which can obtain the uncertainty distribution of the observed data. The loss function of traditional SVM methods is that the uncertainty distribution of the data cannot be obtained. The results show that the SVM algorithm with the new loss function has better accuracy in processing the observed data. For the maximum prediction error of the observed data, the error with the new loss function is only 2.58%, which is reduced by 0.21% compared to the SVM method with the old loss function. However, the prediction error of the SVM algorithm with the old loss function is 2.78%, and the SVM algorithm with the new loss function has obvious prediction accuracy.

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Smooth twin bounded support vector machine with pinball loss

Cited by 11 publications

References 32 publications

Development of hybrid SVM-FA, DT-FA and MLR-FA models to predict the flexural strength (FS) of recycled concrete

Development of hybrid SVM-FA, DT-FA and MLR-FA models to predict the flexural strength (FS) of recycled concrete

Capped Asymmetric Elastic Net Support Vector Machine for Robust Binary Classification

Application of a New Loss Function-Based Support Vector Machine Algorithm in Quality Control of Measurement Observation Data

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