A novel projection nonparallel support vector machine for pattern classification

Hou, Qiuling; Liu, Liming; Ling, Zhen; Jing, Ling

doi:10.1016/j.engappai.2018.08.003

Cited by 14 publications

(7 citation statements)

References 40 publications

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“…However, SVM classifiers can fulfil this objective, perfectly. The SVM finds an optimal separating hyperplane by maximising the margin between the separating hyperplane and the data [26, 27]. Three multi‐class classification problems, namely SVM 1 , SVM 2 , and SVM 3 are defined and solved to determine the type, location, and severity of the internal faults, respectively.…”

Section: Principles Of the Proposed Hybrid Techniquementioning

confidence: 99%

An EKF‐SVM machine learning‐based approach for fault detection and classification in three‐phase power transformers

Kazemi

Naseri

Yazdi

et al. 2021

IET Science Measure & Tech

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In this paper, a hybrid approach for effective diagnosis of power transformers is proposed. In the proposed method, the extended Kalman filter is used for the estimation of three‐phase currents in the primary windings of the transformer. Three residual signals are defined as the differences between the measured and estimated three‐phase currents. When the transformer is healthy, the EKF perfectly estimates the primary currents and hence, the residual signals are nearly zero. However, when the transformer is faulty, the EKF cannot suitably estimate the currents due to the large model mismatch resulting from the transformer internal faults. Consequently, large residual signals are generated, which are used as the key signatures for discriminating the internal faults from energisation conditions. Besides, the proposed method uses the entries of the covariance matrix of the estimation error to locate and classify the internal faults. For these purposes, support vector machine classifiers are used. The effectiveness of the proposed method is demonstrated by a large number of simulation test cases obtained using PSCAD/EMTDC software. Also, hardware‐in‐the‐loop experiments are conducted using dSPACE1104 development platform and real‐time feasibility of the proposed method is authenticated.

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Section: Principles Of the Proposed Hybrid Techniquementioning

confidence: 99%

An EKF‐SVM machine learning‐based approach for fault detection and classification in three‐phase power transformers

Kazemi

Naseri

Yazdi

et al. 2021

IET Science Measure & Tech

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“…Compared with the classical SVM, the nonparallel SVM models (GEPSVM and TWSVM) have lower computational complexity and better generalization ability. Therefore, in the last few years, they have been studied extensively and developed rapidly, including a least squares version of TWSVM (LSTSVM) [16], structural risk minimization version of TWSVM (TBSVM) [17], ν-PTSVM [18], nonparallel SVM (NPSVM) [19,20], nonparallel projection SVM (NPrSVM) [21], and so on [21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A Multiclass Nonparallel Parametric-Margin Support Vector Machine

Zhang

Chen

et al. 2021

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The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore, this paper extends TPMSVM for multiclass classification and proposes a novel K multiclass nonparallel parametric-margin support vector machine (MNP-KSVC). Specifically, our MNP-KSVC enjoys the following characteristics. (1) Under the “one-versus-one-versus-rest” multiclass framework, MNP-KSVC encodes the complicated multiclass learning task into a series of subproblems with the ternary output {−1,0,+1}. In contrast to the “one-versus-one” or “one-versus-rest” strategy, each subproblem not only focuses on separating the two selected class instances but also considers the side information of the remaining class instances. (2) MNP-KSVC aims to find a pair of nonparallel parametric-margin hyperplanes for each subproblem. As a result, these hyperplanes are closer to their corresponding class and at least one distance away from the other class. At the same time, they attempt to bound the remaining class instances into an insensitive region. (3) MNP-KSVC utilizes a hybrid classification and regression loss joined with the regularization to formulate its optimization model. Then, the optimal solutions are derived from the corresponding dual problems. Finally, we conduct numerical experiments to compare the proposed method with four state-of-the-art multiclass models: Multi-SVM, MBSVM, MTPMSVM, and Twin-KSVC. Experimental results demonstrate the feasibility and effectiveness of MNP-KSVC in terms of multiclass accuracy and learning time.

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“…From then on, various improved algorithms based on PTSVM are proposed [24]- [34], e.g. RPTSVM [24], LSPTSVM [25], [26], IPTSVM [27], LIWLSPTSVM [28], PNPSVM [29], NPTSVM [30], PTSVR [31] and other variants PTSVM algorithms [32]- [34]. Although LSTSVM has been presented by using the squared loss function instead of hinge loss function in TWSVM and obtains very fast training speed since two QPPs are replaced by two systems of linear equations, but may result in the reduction of classification ability and the characteristic of constructing two nonparallel hyperplanes may be weakened [35].…”

Section: Introductionmentioning

confidence: 99%

Weighted Linear Loss Projection Twin Support Vector Machine for Pattern Classification

2019

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Based on the recently proposed projection twin support vector machine (PTSVM) and least squares projection twin support vector machine (LSPTSVM), in this paper, we propose a weighted linear loss projection twin support vector machine, namely WLPTSVM for short. By introducing the weighted linear loss function, the proposed WLPTSVM not only solves systems of linear equations with lower computational cost but also obtains comparable classification accuracy. In addition, it is able to dispose of large scale classification problems efficiently without any extra external optimizers. The experiments conducted on synthetic and several benchmark datasets illustrate the effectiveness of our WLPTSVM.INDEX TERMS Pattern classification, twin support vector machine, projection twin support vector machine, weighted linear loss function.

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A novel projection nonparallel support vector machine for pattern classification

Cited by 14 publications

References 40 publications

An EKF‐SVM machine learning‐based approach for fault detection and classification in three‐phase power transformers

An EKF‐SVM machine learning‐based approach for fault detection and classification in three‐phase power transformers

A Multiclass Nonparallel Parametric-Margin Support Vector Machine

Weighted Linear Loss Projection Twin Support Vector Machine for Pattern Classification

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