A novel projection twin support vector machine for binary classification

Chen, Sugen; Wu, Xiao-Jun; Yin, He-Feng

doi:10.1007/s00500-017-2974-z

Cited by 10 publications

(7 citation statements)

References 28 publications

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“…For example, some scholars [22,23] believe that artificial intelligence is a science that allows machines to do things that would otherwise require human intelligence. Other researchers [24,25] believe that artificial intelligence technology refers to the development and creation of advanced machine programs that can replace human intelligent thinking.…”

Section: The Current Situation Of Aging In Chinamentioning

confidence: 99%

The Theme Evolution and Strategic Decision-Making of the Public’s Attention to the Theme of Aging in China Based on Big Data Analysis

2022

Mathematical Problems in Engineering

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Actively responding to population aging has become the new national condition of China’s aging society in the new era. To do a good job of actively coping with aging in the new era, it is necessary to take multiple measures and work together on the basis of clarifying the major realities and problems faced by China’s aging society. From the perspective of big data technology, this paper systematically studies the concerns of China’s aging through the method of artificial intelligence. In addition, with two typical aging indicators, three coupled intelligent algorithms are introduced in this paper. The prediction results show that the multi-dimensional support vector machine optimized by the immune algorithm shows good advantages compared with the other two algorithms. The multi-dimensional support vector machine optimized by the immune algorithm, one of the big data, has a good prediction effect on the performance indicators of China’s aging, which indirectly illustrates the applicability of big data analysis to the concerns of China’s aging.

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Section: The Current Situation Of Aging In Chinamentioning

confidence: 99%

The Theme Evolution and Strategic Decision-Making of the Public’s Attention to the Theme of Aging in China Based on Big Data Analysis

2022

Mathematical Problems in Engineering

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“…Once w 1 , b 1 and w 2 , b 2 are obtained from (32) and 33, the two nonparallel hyperplanes (1) are known. A new data point x ∈ R n is assigned to positive class W 1 or negative class W 2 by…”

Section: Eftwsvm-cilmentioning

confidence: 99%

“…In order to deal with complex XOR problem, inspired by multi-weight vector projection SVM (MVSVM) [24], Chen et al [25] proposed the projection twin SVM (PTSVM), which seeks a projection direction rather than a hyperplane for each class. Followed by, many variants of PTSVM were proposed, such as LSPTSVM [26], [27], LIWLSPTSVM [28], RPTSVM [29], L1-TPSVM [30], PTSVR [31], NPTSVM [32] and L21-EMVSVM [33]. Moreover, some overviews on twin support vector machine can be found in references [34]- [36].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

et al. 2019

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Twin support vector machine (TWSVM) is a new machine learning method, as opposed to solving a single quadratic programming problem in support vector machine (SVM), which generates two nonparallel hyperplanes by solving two smaller size quadratic programming problems. However, the TWSVM obtains the final classifier by giving the same importance to all training samples which may be important for classification performance. In order to address this problem, in this paper, we propose a novel entropy-based fuzzy twin bounded support vector machine (EFTBSVM) for binary classification problems. By considering the fuzzy membership value for each sample and assigning it based on the entropy value, the samples with higher class certainty are assigned to relatively larger fuzzy membership. In addition, the proposed EFTBSVM not only maintains the superior characteristics of the TWSVM but also exploits the structural risk minimization principle by introducing a regularization term. The experimental results achieved on synthetic datasets and benchmark datasets illustrate the effectiveness of the proposed method.

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“…• • • , υ 1m 1 ) T are determined by ( 29) and (30). Similar to the linear case, assuming that J 1 and J 2 are determined, we can obtain the solutions to the problems ( 40) and (41) as follows.…”

Section: B Nonlinear Wlptsvmmentioning

confidence: 99%

“…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 twin support vector machine for binary classification

Cited by 10 publications

References 28 publications

The Theme Evolution and Strategic Decision-Making of the Public’s Attention to the Theme of Aging in China Based on Big Data Analysis

The Theme Evolution and Strategic Decision-Making of the Public’s Attention to the Theme of Aging in China Based on Big Data Analysis

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

Weighted Linear Loss Projection Twin Support Vector Machine for Pattern Classification

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