Least squares twin support vector machines for pattern classification

Kumar, Manoj; Gopal, M.

doi:10.1016/j.eswa.2008.09.066

Cited by 545 publications

(40 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their difference is that TSVM uses simpler formulation than GEPSVM, and the former can be solved by merely two QP problems. Our results align with the finding in Kumar and Gopal [59], which says "generalization performance of TSVM is better than GEPSVM and conventional SVM". Nevertheless, Ding et al [60] claimed that TSVM has a lower generalization ability, so it is too early to make a decision about the classification performance of TSVM before more rigorous tests are implemented.…”

Section: Discussionsupporting

confidence: 80%

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

Wang

Zhang

Yang

et al. 2015

Entropy

View full text Add to dashboard Cite

Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper. Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform (FRFT) and Shannon entropy. Afterwards, the Welch's t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10ˆK-fold stratified cross validation test showed that this proposed "FRFE + WTT + TSVM" yielded an accuracy of 100.00%, 100.00%, and 99.57% on datasets that contained 66, 160, and 255 brain images, respectively. Conclusions: The proposed "FRFE + WTT + TSVM" method is superior to 20 state-of-the-art methods.

show abstract

Section: Discussionsupporting

confidence: 80%

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

Wang

Zhang

Yang

et al. 2015

Entropy

View full text Add to dashboard Cite

show abstract

“…The difference between them is TSVM uses simpler formulation than GEPSVM, and the former can be solved by merely two QP problems. Our results align with the finding in Kumar and Gopal [50], which says "generalization performance of TSVM is better than GEPSVM and conventional SVM". In following experiments, TSVM is the default classifier…”

Section: Classifier Comparisonsupporting

confidence: 80%

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

et al. 2016

View full text Add to dashboard Cite

(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256ˆ256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s "variance and entropy (VE)" features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our "DTCWT + VE + TSVM" obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible.

show abstract

“…Therefore, TWSVM works faster than the standard SVM. Subsequently, there are many extensions for TWSVM including the improvements on TWSVM (TBSVM) [13], the least square TWSVM (LS-TWSVM) [14–17], nonparallel plane proximal classifier (NPPC) [18], smooth TWSVM [19], geometric algorithm [20], and twin support vector regression (TWSVR) [21]. TWSVM was also extended to deal with multiclassification TWSVM [22–24].…”

Section: Introductionmentioning

confidence: 99%

“…For binary classification, if the loss function is the hinge loss function, then the framework can become TWSVM [12] or TBSVM [13] with different parameters; if the loss function is the square loss function, then the framework is LS-TWSVM [14]; if the loss function is the convex combination of the linear and square loss functions, then the framework is NPPC [18]. Actually, we can also get smooth TWSVM [19] by replacing 2-norm with 1-norm in the framework.…”

Section: Introductionmentioning

confidence: 99%

A Learning Framework of Nonparallel Hyperplanes Classifier

Yang

Shao

Jiang

2015

The Scientific World Journal

View full text Add to dashboard Cite

A novel learning framework of nonparallel hyperplanes support vector machines (NPSVMs) is proposed for binary classification and multiclass classification. This framework not only includes twin SVM (TWSVM) and its many deformation versions but also extends them into multiclass classification problem when different parameters or loss functions are chosen. Concretely, we discuss the linear and nonlinear cases of the framework, in which we select the hinge loss function as example. Moreover, we also give the primal problems of several extension versions of TWSVM's deformation versions. It is worth mentioning that, in the decision function, the Euclidean distance is replaced by the absolute value |w T x + b|, which keeps the consistency between the decision function and the optimization problem and reduces the computational cost particularly when the kernel function is introduced. The numerical experiments on several artificial and benchmark datasets indicate that our framework is not only fast but also shows good generalization.

show abstract

Least squares twin support vector machines for pattern classification

Cited by 545 publications

References 9 publications

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

A Learning Framework of Nonparallel Hyperplanes Classifier

Contact Info

Product

Resources

About