Quadratic kernel-free non-linear support vector machine

Dagher, Issam

doi:10.1007/s10898-007-9162-0

Cited by 75 publications

(43 citation statements)

References 9 publications

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“…In this section, we investigate the credit scoring performance of the proposed fuzzy non-kernel QSSVM model on some realworld credit data sets. For comparisons, we mainly test three groups of credit scoring methods: The first group includes commonly-used non-SVM methods such as the logistic regression (denoted by "LOG REG") and feed-forward backpropagation (FFBP) neural networks (denoted by "FFBP NN"); the second group includes commonly-used kernel-based SVM methods such as the soft SVM model with Gaussian kernel (denoted by "SVM GausKer"), the weighted 2-norm SVM model with Gaussian or Quadratic kernel [27] (denoted by "W2NSVM GausKer" or "W2NSVM QuadKer"), well-known FSVM model with within-class scatter and Gaussian kernel [1] (denoted by "FSVMWCS GausKer") and clustered SVM method [11] (denoted by "Clu SVM"); the third group includes two non-kernel SVM models such as Dagher's QSVM model [6] and soft QSSVM model [17]. For all SVM models, the grid method is utilized to search the best penalty parameterη as the following: log 2η ∈ {2, 3, .…”

Section: 4mentioning

confidence: 99%

Corporate and personal credit scoring via fuzzy non-kernel SVM with fuzzy within-class scatter

Luo¹,

Yang²,

Wang³

2020

Journal of Industrial &Amp; Management Optimization

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Nowadays, the effective credit scoring becomes a very crucial factor for gaining competitive advantages in credit market for both customers and corporations. In this paper, we propose a credit scoring method which combines the non-kernel fuzzy 2-norm quadratic surface SVM model, T-test feature weighting strategy and fuzzy within-class scatter together. It is worth pointing out that this new method not only saves computational time by avoiding choosing a kernel and corresponding parameters in the classical SVM models, but also addresses the "curse of dimensionality" issue and improves the robustness. Besides, we develop an efficient way to calculate the fuzzy membership of each training point by solving a linear programming problem. Finally, we conduct several numerical tests on two benchmark data sets of personal credit and one real-world data set of corporation credit. The numerical results strongly demonstrate that the proposed method outperforms eight state-of-the-art and commonly-used credit scoring methods in terms of accuracy and robustness.

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Section: 4mentioning

confidence: 99%

Corporate and personal credit scoring via fuzzy non-kernel SVM with fuzzy within-class scatter

Luo¹,

Yang²,

Wang³

2020

Journal of Industrial &Amp; Management Optimization

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“…Generally, the SVM is used to separate the extracted feature sets into two classes through finding an optimal hyperplane. A study by Dagher [42] presented a quadratic kernel-free non-linear SVM which was used in this research. The quadratic function was utilised to split the feature sets non-linearly as can be found in [42].…”

Section: Support Vector Machinementioning

confidence: 99%

“…A study by Dagher [42] presented a quadratic kernel-free non-linear SVM which was used in this research. The quadratic function was utilised to split the feature sets non-linearly as can be found in [42]. Furthermore, various output codes are examined to solve the multiclass categorisation problem [43].…”

Section: Support Vector Machinementioning

confidence: 99%

Epileptic EEG signal classification using optimum allocation based power spectral density estimation

Ghayab

Siuly

et al. 2018

IET signal process.

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This study proposes a novel approach blending optimum allocation (OA) technique and spectral density estimation to analyse and classify epileptic electroencephalogram (EEG) signals. This study employs the OA to determine representative sample points from the original EEG data and then applies periodogram (PD), autoregressive (AR), and the mixture of PD and AR to extract the discriminative features from each OA sample group. The obtained feature sets are evaluated by three popular machine learning methods: support vector machine (SVM), quadratic discriminant analysis (QDA), and k-nearest neighbour (k-NN). Several output coding approaches of the SVM classifier are tested for selecting the best feature sets. This scheme was implemented on a benchmark epileptic EEG database for evaluation and also compared with existing methods. The experimental results show that the OA_AR feature set yields better performances by the SVM with an overall accuracy of 100%, and outperforms the state-of-the-art works with a 14.1% improvement. Thus, the findings of this study prove that the proposed OA-based AR scheme has significant potential to extract features from EEG signals. The proposed method will assist experts to automatically analyse a large volume of EEG data and benefit epilepsy research.

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“…To take advantage of the idea of SVM while avoiding the challenges in using the nonlinear kernel trick, a kernel-free quadratic surface SVM (QSSVM) model was proposed by Dagher [7]. Luo et al recently developed its extension, the so called soft margin quadratic surface SVM (SQSSVM), that incorporates noise and outliers [21].…”

mentioning

confidence: 99%

Quadratic surface support vector machine with L1 norm regularization

Mousavi

Gao

Han

et al. 2022

JIMO

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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.

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Quadratic kernel-free non-linear support vector machine

Cited by 75 publications

References 9 publications

Corporate and personal credit scoring via fuzzy non-kernel SVM with fuzzy within-class scatter

Corporate and personal credit scoring via fuzzy non-kernel SVM with fuzzy within-class scatter

Epileptic EEG signal classification using optimum allocation based power spectral density estimation

Quadratic surface support vector machine with L1 norm regularization

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