Improvement on projection twin support vector machine

Xie, Xue‐Jun

doi:10.1007/s00521-017-3237-8

Cited by 8 publications

(7 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The parameters of each algorithm are set as follows: For the BPMLL, the number of hidden neurons is set to 20% of the input dimension, and the number of training epochs is 100. For the Rank-SVM, the kernel function parameter and penalty parameter c are selected from 2 −6 , ..., 2 0 , ..., 2 6 . For the MLTSVM, the penalty parameters c k and regularization parameter λ k are selected from 2 −6 , ..., 2 0 , ..., 2 6 .…”

Section: Parameter Settingmentioning

confidence: 99%

“…For the Rank-SVM, the kernel function parameter and penalty parameter c are selected from 2 −6 , ..., 2 0 , ..., 2 6 . For the MLTSVM, the penalty parameters c k and regularization parameter λ k are selected from 2 −6 , ..., 2 0 , ..., 2 6 . For the SS-MLLSTSVM, the penalty parameters c k1 and regularization parameters c k2 , c k3 are selected from 2 −6 , ..., 2 0 , ..., 2 6 .…”

Section: Parameter Settingmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Semi-Supervised Multi-Label Least Squares Twin Support Vector Machine

Kang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Multi-label twin support vector machine (MLTSVM), being an effective multi-label classifier based on twin support vector machine (TSVM), has been widely studied and applied due to its excellent classification performance. However, there are some disadvantages in classical MLTSVM: (a) MLTSVM needs to solve a series of quadratic programming problems (QPPs), which makes its learning speed lower. (b) For multi-label learning problems, it is very difficult to obtain all labels of all samples. In fact, the datasets that we can obtain only contain a small amount of labeled samples and a large amount of partially labeled and unlabeled samples. However, MLTSVM can only use expensive labeled samples and ignore cheap unlabeled and partially labeled samples. For the drawbacks, we propose a novel semi-supervised multilabel least squares twin support vector machine, called SS-MLLSTSVM. Firstly, to speed up solving, SS-MLLSTSVM introduces the least squares idea into each sub-classifier of MLTSVM, which makes each subclassifier only need to solve a system of linear equations, instead of one QPP. Secondly, SS-MLLSTSVM can make full use of the geometric information in unlabeled and partially labeled samples by introducing manifold regularization term into each sub-classifier. The experimental results on the benchmark datasets show that, compared with the existing multi-label classification algorithms, our SS-MLLSTSVM has better classification performance.

show abstract

Section: Parameter Settingmentioning

confidence: 99%

Section: Parameter Settingmentioning

confidence: 99%

An Effective Semi-Supervised Multi-Label Least Squares Twin Support Vector Machine

Kang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Support vector machine (SVM) proposed in 1995 is a machine learning algorithm based on the VC dimension theory of statistical theory and the structural risk minimization principle [28]. In SVM, a classification hyper-plane is created and regarded as the decision surface, which separates positive from negative samples and maximizes the isolation edge between them [29].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm Based on Squirrel Search Algorithm and Invasive Weed Optimization for Optimization

Hu¹,

Zhang²,

Bai³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

This paper proposes a hybrid algorithm called ISSA based on the combination of squirrel search algorithm (SSA) proposed in 2019 and invasive weed optimization (IWO) proposed in 2006. About 36 benchmark functions are employed to test the performances of ISSA. Then, ISSA is combined with support vector machine (SVM) and deterministic maximum-likelihood (DML) algorithm, respectively, and the two corresponding models ISSA-SVM and ISSA-DML are established for performing the grade classifications of air quality and the direction of arrival (DOA) estimation of MEMS vector hydrophone, respectively. The results of 36 benchmark functions prove that the proposed ISSA is able to provide very competitive results in terms of the average values, the standard derivation, and the convergence curves. The average accuracy rate of classification of ISSA-SVM model is the best and reaches 87.91971%, and the DOA estimations of ISSA-DML have the least root mean square error (RMSE) and the closest to the actual angles. Therefore, it is concluded that the proposed ISSA is an effective algorithm for function optimizations and is suitable to be combined with other algorithms and machine learning for classification and estimation.INDEX TERMS Squirrel search algorithm, invasive weed optimization, support vector machine, deterministic maximum-likelihood, optimization.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Improvement on projection twin support vector machine

Cited by 8 publications

References 35 publications

An Effective Semi-Supervised Multi-Label Least Squares Twin Support Vector Machine

An Effective Semi-Supervised Multi-Label Least Squares Twin Support Vector Machine

A Hybrid Algorithm Based on Squirrel Search Algorithm and Invasive Weed Optimization for Optimization

Weighted Linear Loss Projection Twin Support Vector Machine for Pattern Classification

Contact Info

Product

Resources

About