A kernel-free double well potential support vector machine with applications

Gao, Zheming; Fang, Shu‐Cherng; Luo, Jian; Medhin, Negash G.

doi:10.1016/j.ejor.2020.10.040

Cited by 35 publications

(19 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since (w * , b * , c * , α * ) satisfies (10), it is straightforward to verify that (w * , b * , c * , ξ * = 0 m , α * , η * ) satisfies (12) and hence is an optimal solution of L1-SQSSVM . Since G is positive definite, we know that w * and b * are unique by Theorem 4.2.…”

Section: 2mentioning

confidence: 99%

“…The proposed L1-SQSSVM model is capable of handling data with many features since the 1 norm regularization can find the important features, or the interactions between the features to improve the classification accuracy. It has many potential real-world applications in the field of industrial and management optimization, such as sentiment classification of customer reviews [14], credit scoring [12], disease diagnosis [2], etc.…”

mentioning

confidence: 99%

“…We randomly pick k% (k = 10, 20, 40) [7,21] of the full data set as the training set. The parameters λ and µ are tuned by using grid method [21,12]. The kernel parameters are selected by the package over training the full data set.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Quadratic surface support vector machine with L1 norm regularization

Mousavi

Gao

Han

et al. 2022

JIMO

Self Cite

View full text Add to dashboard Cite

We propose 1 norm regularized quadratic surface support vector machine models for binary classification in supervised learning. We establish some desired theoretical properties, including the existence and uniqueness of the optimal solution, reduction to the standard SVMs over (almost) linearly separable data sets, and detection of true sparsity pattern over (almost) quadratically separable data sets if the penalty parameter on the 1 norm is large enough. We also demonstrate their promising practical efficiency by conducting various numerical experiments on both synthetic and publicly available benchmark data sets.

show abstract

Section: 2mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Quadratic surface support vector machine with L1 norm regularization

Mousavi

Gao

Han

et al. 2022

JIMO

Self Cite

View full text Add to dashboard Cite

show abstract

“…(9) (10) In this way, two nonparallel separating hyperplanes are obtained. As we know the linear LS-TSVM completely solves the classification problem with just two systems of linear equation as opposed to solving two QPPs in TSVM or one in QPP in SVM which helps the proposed PLS-TSVM to be faster than the other two algorithm in the training phase.…”

Section: Linear Pls-tsvmmentioning

confidence: 99%

“…For classification tasks, different machine learning classifiers have been applied. The recent research indicates that Support Vector Machines has better performance among other classifiers in most cases [6][7][8][9][10][11]. However, SVM-based classifiers suffer from several major problems.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Shakibian

Nasiri

2022

IJE

View full text Add to dashboard Cite

Support Vector Machine classifiers are widely used in many classification tasks. However, they have two considerable weaknesses, Unclassifiable Region (UR) in multiclass classification and outliers. In this research, we address these problems by introducing Probabilistic Least Square Twin Support Vector Machine (PLS-TSVM). The proposed algorithm introduces continuous and probabilistic outputs over the model obtained by Least-Square Twin Support Vector Machine (LS-TSVM) method with both linear and polynomial kernel functions. PLS-TSVM not only solves the unclassifiable region problem by introducing a continuous output value membership function, but it also reduces the adverse effects of noisy data and outliers. For showing the superiority of our proposed method, we have conducted experiments on various UCI datasets. In the most cases, higher or competitive accuracy to other methods have been obtained such that in some UCI datasets, PLS-TSVM could obtain up to 99.90% of classification accuracy. Moreover, PLS-TSVM has been evaluated against "one-against-all" and "oneagainst-one" approaches on several well-known video datasets such as Weizmann, KTH, and UCF101 for human action recognition task. The results show the higher accuracy of PLS-TSVM compared to its counterparts. Specifically, the proposed algorithm could improve respectively about 12.2%, 2.8%, and 12.1% of classification accuracy in three video datasets compared to the standard SVM and LS-TSVM classifiers. The final results indicate that the proposed algorithm could achieve better overall performances than the literature.

show abstract

EWT‐SMOTE to improve default prediction performance in imbalanced data: Analysis of Chinese data

Zhou,

Lin,

Chi

et al. 2023

Journal of Forecasting

View full text Add to dashboard Cite

This study aims to solve the imbalanced sample problem in default prediction. We calculate the classification contribution score of each default customer by the entropy weight technique (EWT) for order of preference by similarity to the ideal solution and construct a default prediction model according to several models. Our proposed EWT‐synthetic minority oversampling technique (SMOTE) method significantly improves the prediction accuracy of several typical default prediction models and reduces type II error. We find that the indicators “net cash flow from operating activities,” “Engel coefficient,” “basic earnings per share,” and “total social retail sales” significantly influence default prediction of Chinese listed companies.

show abstract

A kernel-free double well potential support vector machine with applications

Cited by 35 publications

References 25 publications

Quadratic surface support vector machine with L1 norm regularization

Quadratic surface support vector machine with L1 norm regularization

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

EWT‐SMOTE to improve default prediction performance in imbalanced data: Analysis of Chinese data

Contact Info

Product

Resources

About