Multiple recursive projection twin support vector machine for multi-class classification

Li, Chun‐Na; Huang, Yunfeng; Wu, He-Ji; Shao, Yuan‐Hai; Yang, Ziqiang

doi:10.1007/s13042-014-0289-2

Cited by 26 publications

(3 citation statements)

References 34 publications

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“…In addition, the training samples of each classifier are balanced, the training precision is high, the entire systems performance is better than other methods for classification problems with fewer categories. Practical application indicates that the ovo method is more propitious to application [30]. Therefore, this paper adopts the one-versus-one method to identify the mental workload of oceanauts.…”

Section: Multi-class Support Vector Machinesmentioning

confidence: 99%

Mental Workload Classification of Oceanauts from EEG Data Using Multiclass Support Vector Machines

Liu

Shi

et al. 2022

Preprint

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BackgroundIn the actual operation task, the workload of the oceanaut is mainly mental workload. For the oceanaut, too high or too low mental workload will significantly reduce work efficiency and even lead to major safety accidents. Classification of mental workload of the oceanaut in operational task research is one of the key problems in operational task research. However, the traditional mechanism modeling method is complex, computational complexity and low accuracy.MethodIn this paper, machine learning method got used to the model. Based-on Electroencephalograph (EEG) data collected from the simulation experiment of the operating manipulator, multiclass support vector machine (MSVM) was used to train the samples, and the optimal training sample size was selected. The parameters of the model were optimized by grid search mixed with particle-swarm optimization algorithm (GSPSO) to obtain the optimal classification of oceanaut’s mental workload.ResultsAs the research results showed that the method could accurately classify the mental workload of oceanauts. Furthermore, GSPSO-MSVM in the classification of oceanaut’s mental workload had the advantage over K-nearest neighbors (KNN), BP neural network (BP), random forest (RF) and support vector machines (SVM).ConclusionGSPSO-MSVM in the classification of oceanaut’s mental workload had the advantage over K-nearest neighbors (KNN), BP neural network (BP), random forest (RF) and support vector machines (SVM).Key points: The significance of the studyClassification of mental workload of the oceanaut in operational task research is one of the key problems in operational task research.Based-on Electroencephalograph (EEG) data collected from the simulation experiment of the operating manipulator, multiclass support vector machine (MSVM) was used to train the samples, and the optimal training sample size was selected.The parameters of the model were optimized by grid search mixed with particle-swarm optimization algorithm (GSPSO) to obtain the optimal classification of oceanaut’s mental workload.GSPSO-MSVM in the classification of oceanaut’s mental workload had the advantage over K-nearest neighbors (KNN), BP neural network (BP), random forest (RF) and support vector machines (SVM).

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Section: Multi-class Support Vector Machinesmentioning

confidence: 99%

Mental Workload Classification of Oceanauts from EEG Data Using Multiclass Support Vector Machines

Liu

Shi

et al. 2022

Preprint

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“…It has similar performance as MP-TSVM. Based on P-TSVM, Li et al [84] in 2016, proposed multiple recursive projection TSVM (Multi-P-TSVM) which solves k QPPs in order to determine k-projection axes (for k classes) . Authors introduced regularization term and recursive procedure which increases the generalization but this algorithm is complex when more orthogonal projection axes are generated.…”

Section: Twin Support Vector Machine For Multi-class Classificationmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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“…Madzarov et al [38] presented a novel architecture of SVM classifiers based on Binary Decision Tree architecture, takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. Recently, a novel multiple projection twin support vector machine (PTSVM) was proposed [39], which improves the generalization ability great.…”

Section: Multi-class Support Vector Machinementioning

confidence: 99%

Research Survey on Support Vector Machine

Wang¹,

Xiong²,

Yao³

et al. 2017

Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications

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Support vector machine (SVM) as a learning machine has shown a good learning ability and generalization ability in classification, regression and forecasting. Because of its excellent learning performance, SVM has always been a hotspot in machine learning. In hence, this paper will make a more systematic introduction of SVM, including the theory of SVM, the summarization and comparison of the quadratic programming optimizations and parameter optimizations for SVM and the introduction of some new SVMs, like FSVM, TSVM, MSVM, etc. Then, the applications of SVM in real life will be presented, especially, in the area of mobile multimedia. Finally, we conclude with a discussion of the direction of further SVM improvement.

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Multiple recursive projection twin support vector machine for multi-class classification

Cited by 26 publications

References 34 publications

Mental Workload Classification of Oceanauts from EEG Data Using Multiclass Support Vector Machines

Mental Workload Classification of Oceanauts from EEG Data Using Multiclass Support Vector Machines

Comprehensive Review On Twin Support Vector Machines

Research Survey on Support Vector Machine

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