Least Squares Twin Support Vector Machine for Multi-Class Classification

Chen, Sugen; Xu, Jingkun

doi:10.14257/ijdta.2015.8.5.06

Cited by 7 publications

(4 citation statements)

References 13 publications

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“…e main idea is to use the clustering method to divide the original data into several particles and select the particles with more information to participate in the classification of SVM. Twin support vector machine, also known as double interface support vector machine, has faster training speed, making it better able to deal with large-scale data [24]. However, it does not have the characteristics of standard SVM, so the twin model needs to be further improved.…”

Section: Related Workmentioning

confidence: 99%

Design of a Treatment Method for Patients with Anxiety Disorders Based on Ball Games and Digital Technology Assistance

Chang

2022

Security and Communication Networks

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Patients with anxiety disorders often have a long and often recurring course of the disease, which negatively affects all aspects of the patient’s work life while also causing a significant economic burden. Psychotherapy and medication are common treatments for anxiety disorders, but they are ineffective, especially in cases of chronic anxiety, having a high relapse rate and often causing adverse side effects. Ball games are important sports that have been widely used in training and sports activities. Physical activity has been discovered to be good not only for physical health but also for the treatment of anxiety disorders, according to recent medical studies. Therefore, there is an urgent need to investigate the rehabilitation effects of ball games on anxiety disorder patients and to develop novel training methods. Meanwhile, in recent years, the use of computers for digital technology-assisted mental illness treatment has received widespread attention. In this paper, a new treatment strategy for anxiety patients is proposed by combining ball games with support vector machine algorithm as digital assisted technology. This research examines the shortcomings in the classic support vector machine technique first and then proposes an improved FSVM algorithm for data processing and analysis. The results of the experiments reveal that the proposed technique has a significant impact on the treatment of anxiety disorders and creates the foundation for the study of anxiety patients’ exercise therapy.

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Section: Related Workmentioning

confidence: 99%

Design of a Treatment Method for Patients with Anxiety Disorders Based on Ball Games and Digital Technology Assistance

Chang

2022

Security and Communication Networks

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“…A set of simpler QPPs is solved to determine the hyperplanes. This method is more efficient when compared to regular SVM because the information from one class provides constraints to the other class, which reduces the computation complexity of the algorithm by four times [20,21].…”

Section: Theory and Modelingmentioning

confidence: 99%

“…LSTSVM generates two non-parallel hyperplanes by solving two linear systems of equations, and the solutions are obtained through the following optimization problems [20,21]:…”

Section: Least Squares Twin Support Vector Machinesmentioning

confidence: 99%

“…Therefore, for a complex situation such as the phosphorous end-point prediction, TWSVM is preferred over general SVM. LSTSVM is a more appropriate algorithm than TWSVM for data sets with unbalanced classes, as it chooses a different parameter for each class [20,21]. LSTSVM and TWSVM are also preferred over regular SVM when it comes to the large scale and high complexity of QPPs.…”

Section: Introductionmentioning

confidence: 99%

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Least Squares Twin Support Vector Machines to Classify End-Point Phosphorus Content in BOF Steelmaking

Barui

Mukherjee

et al. 2022

Metals

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End-point phosphorus content in steel in a basic oxygen furnace (BOF) acts as an indicator of the quality of manufactured steel. An undesirable amount of phosphorus is removed from the steel by the process of dephosphorization. The degree of phosphorus removal is captured numerically by the ‘partition ratio’, given by the ratio of %wt phosphorus in slag and %wt phosphorus in steel. Due to the presence of multitudes of process variables, often, it is challenging to predict the partition ratio based on operating conditions. Herein, a robust data-driven classification technique of least squares twin support vector machines (LSTSVM) is applied to classify the ‘partition ratio’ to two categories (‘High’ and ‘Low’) steels indicating a greater or lesser degree of phosphorus removal, respectively. LSTSVM is a simpler, more robust, and faster alternative to the twin support vector machines (TWSVM) with respect to non-parallel hyperplanes-based binary classifications. The relationship between the ‘partition ratio’ and the chemical composition of slag and tapping temperatures is studied based on approximately 16,000 heats from two BOF plants. In our case, a relatively higher model accuracy is achieved, and LSTSVM performed 1.5–167 times faster than other applied algorithms.

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RETRACTED ARTICLE: Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

Ambika¹,

Raghuraman²,

SaiRamesh

2020

Soft Comput

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Least Squares Twin Support Vector Machine for Multi-Class Classification

Abstract: Twin support vector machine (TWSVM)

Cited by 7 publications

References 13 publications

Design of a Treatment Method for Patients with Anxiety Disorders Based on Ball Games and Digital Technology Assistance

Design of a Treatment Method for Patients with Anxiety Disorders Based on Ball Games and Digital Technology Assistance

Least Squares Twin Support Vector Machines to Classify End-Point Phosphorus Content in BOF Steelmaking

RETRACTED ARTICLE: Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

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