ECG Arrhythmia Classification Using Least Squares Twin Support Vector Machines

Refahi, Mohammad Saleh; Nasiri, Jalal A.; Ahadi, Seyed Mohammad

doi:10.1109/icee.2018.8472615

Cited by 18 publications

(7 citation statements)

References 11 publications

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“…TSVM has also implemented for detecting cardiac diseases, one such application was proposed by Houssein et al in [53], heartbeats were detected using Swarm-TSVM and this algorithm achieved better accuracy than TSVM. Refahi et al [136] used LSTSVM and DAG LS-TSVM classifiers for predicting arrhythmia heart disease. Chandra and Bedi [15] proposed linear norm fuzzy based TSVM for color based classification of human skin which achieved better accuracy than other conventional methods.…”

Section: Applications Of Twin Support Vector 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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Section: Applications Of Twin Support Vector Classificationmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Sequence-Based Prediction of ProteinPeptide(SPRINT) method is used to the prediction of Proteinpeptide Residuelevel Interactions by SVM [11]. SVM implements the struc-120 tural risk minimization (SRM) that minimizes the upper bound of generation error [21,22].…”

Section: Related Workmentioning

confidence: 99%

A novel feature fusion based on the evolutionary features for protein fold recognition using support vector machines

Refahi

Mir

Nasiri

2019

Preprint

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Protein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. Some of these approaches are based on extracting features from protein sequences and using a strong classifier. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physiochemical-based information to extract features. In recent years, Finding an efficient technique for integrating discriminate features have been received advancing attention. In this study, we integrate Auto-Cross-Covariance (ACC) and Separated dimer (SD) evolutionary feature extraction methods. The results features are scored by Information gain (IG) to define and select several discriminated features. According to three benchmark datasets, DD, RDD and EDD, the results of the support vector machine (SVM) show more than 6% improvement in accuracy on these benchmark datasets.

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“…For classification tasks, different machine learning classifiers have been applied. The recent research indicates that Support Vector Machines has better performance among other classifiers in most cases [6][7][8][9][10][11]. However, SVM-based classifiers suffer from several major problems.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Shakibian

Nasiri

2022

IJE

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Support Vector Machine classifiers are widely used in many classification tasks. However, they have two considerable weaknesses, Unclassifiable Region (UR) in multiclass classification and outliers. In this research, we address these problems by introducing Probabilistic Least Square Twin Support Vector Machine (PLS-TSVM). The proposed algorithm introduces continuous and probabilistic outputs over the model obtained by Least-Square Twin Support Vector Machine (LS-TSVM) method with both linear and polynomial kernel functions. PLS-TSVM not only solves the unclassifiable region problem by introducing a continuous output value membership function, but it also reduces the adverse effects of noisy data and outliers. For showing the superiority of our proposed method, we have conducted experiments on various UCI datasets. In the most cases, higher or competitive accuracy to other methods have been obtained such that in some UCI datasets, PLS-TSVM could obtain up to 99.90% of classification accuracy. Moreover, PLS-TSVM has been evaluated against "one-against-all" and "oneagainst-one" approaches on several well-known video datasets such as Weizmann, KTH, and UCF101 for human action recognition task. The results show the higher accuracy of PLS-TSVM compared to its counterparts. Specifically, the proposed algorithm could improve respectively about 12.2%, 2.8%, and 12.1% of classification accuracy in three video datasets compared to the standard SVM and LS-TSVM classifiers. The final results indicate that the proposed algorithm could achieve better overall performances than the literature.

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ECG Arrhythmia Classification Using Least Squares Twin Support Vector Machines

Cited by 18 publications

References 11 publications

Comprehensive Review On Twin Support Vector Machines

Comprehensive Review On Twin Support Vector Machines

A novel feature fusion based on the evolutionary features for protein fold recognition using support vector machines

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

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