Insensitive stochastic gradient twin support vector machines for large scale problems

Zhen, Wang; Shao, Yuan‐Hai; Bai, Liang; Li, Chun‐Na; Liu, Liming; Deng, Ning

doi:10.1016/j.ins.2018.06.007

Cited by 44 publications

(6 citation statements)

References 38 publications

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“…Figure 2 visualizes the error rate from the predicted data to actual data for linear classification and Figure 3 visualizes the error rate from the predicted data to actual data for non-linear classification of the benchmark dataset. Table 2 describes the comparative analysis between the other models like SVM, TWSVM [17], TBSVM [21], EFSVM [4], U-NHSVM [10], SGTSVM [22], RTBSVM [15] with p-dist TWSVM based on the following metrics namely Accuracy, F-Measure, G-mean with their Elapsed Time for linear case in seven benchmark data sets. Our proposed method outperforms in linear kernel than Gaussian kernel case.…”

Section: Performance Evaluationmentioning

confidence: 99%

P-dist Based Regularized Twin Support Vector Machine on Imbalanced Binary Dataset

B.¹,

Gajendran²

2023

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Data classification is a significant task in the field of machine learning. Support vector machine is one of the prominent algorithms in classification. Twin support vector machine is a solitary algorithm evolved from support vector machine which has gained popularity owing to its better generalization ability to a greater extent. Twin support vector machine attains quick training speed by explicitly exploring a pair of non-parallel hyperplanes for imbalanced data. In a Twin support vector machine, choosing numerical values for hyper parameters is challenging. Hyper parameter tuning is a prime factor that enhances the performance of a model. However, randomly preferred hyper parameters in the Twin support vector machine are uncertain. This paper proposes a novel p-dist-based regularized Twin support vector machine for imbalanced binary classification problems. Pairwise distances such as Jaccard and Correlation distances are considered for attuning the hyper parameters. The proposed work has been analyzed on many publicly available real-world benchmark datasets for both linear and non-linear cases. The performance of the p-dist-based regularized Twin support vector machine is computationally tested and compared with existing models. The outcome of the proposed model is validated using quality metrics such as Accuracy, F -mean, G-mean, and Elapsed time. Ultimately, the significant result exhibits better performance with less computational time in comparison to several existing methods.

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Section: Performance Evaluationmentioning

confidence: 99%

P-dist Based Regularized Twin Support Vector Machine on Imbalanced Binary Dataset

B.¹,

Gajendran²

2023

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“…For a large scale dataset X, the kernel function K(•, X) transforms the samples into a space with a much higher dimension than linear formation, resulting in a large amount of computations. However, the reduced kernel tricks [43], [44], which replaces K(•, X) with K(•, X), can reduce the computation efficiently, where X is selected from X randomly and its size is much smaller than X.…”

Section: Nonlinear Formationmentioning

confidence: 99%

Multiple Flat Projections for Cross-manifold Clustering

Bai,

Shao,

Chen

et al. 2020

Preprint

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Cross-manifold clustering is a hard topic and many traditional clustering methods fail because of the cross-manifold structures. In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems. In our MFPC, the given samples are projected into multiple subspaces to discover the global structures of the implicit manifolds. Thus, the cross-manifold clusters are distinguished from the various projections. Further, our MFPC is extended to nonlinear manifold clustering via kernel tricks to deal with more complex cross-manifold clustering. A series of non-convex matrix optimization problems in MFPC are solved by a proposed recursive algorithm. The synthetic tests show that our MFPC works on the cross-manifold structures well. Moreover, experimental results on the benchmark datasets show the excellent performance of our MFPC compared with some state-of-the-art clustering methods.

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“…In the following, we extend the RampTWSVC to nonlinear manifold clustering, and the solutions to the problems in linear and nonlinear RampTWSVC are elaborated in next subsetion. The plane-based clustering method can be extend to nonlinear manifold clustering easily by the kernel trick [20], [21]. By introducing a pre-defined kernel function K(•, •), the planebased nonlinear clustering seeks k cluster center manifolds in the kernel generated space as…”

Section: A Formationmentioning

confidence: 99%

Ramp-based twin support vector clustering

Wang

Chen

Shao

et al. 2019

Neural Comput & Applic

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Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost. This letter introduces a ramp cost function into the plane-based clustering to propose a new clustering method, called ramp-based twin support vector clustering (RampTWSVC). RampTWSVC is more robust because of its boundness, and thus it is more easier to find the intrinsic clusters than other planebased clustering methods. The non-convex programming problem in RampTWSVC is solved efficiently through an alternating iteration algorithm, and its local solution can be obtained in a finite number of iterations theoretically. In addition, the nonlinear manifold-based formation of RampTWSVC is also proposed by kernel trick. Experimental results on several benchmark datasets show the better performance of our RampTWSVC compared with other plane-based clustering methods.

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Insensitive stochastic gradient twin support vector machines for large scale problems

Cited by 44 publications

References 38 publications

P-dist Based Regularized Twin Support Vector Machine on Imbalanced Binary Dataset

P-dist Based Regularized Twin Support Vector Machine on Imbalanced Binary Dataset

Multiple Flat Projections for Cross-manifold Clustering

Ramp-based twin support vector clustering

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