Design Development and Performance Analysis of Distributed Least Square Twin Support Vector Machine for Binary Classification

2021 ◽  
Keyword(s):  
Support Vector Machine ◽  
Performance Analysis ◽  
Binary Classification ◽  
Least Square ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Design Development ◽  
And Performance

scholarly journals An Improvement Of Least Square - Twin Support Vector Machine

2021 ◽  
pp. 8-13
Author(s):  
Thế Cường Nguyễn ◽  
Thanh Vi Nguyen
Keyword(s):  
Support Vector Machine ◽  
Binary Classification ◽  
Least Square ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Training Time ◽  
Data Points

In binary classification problems, two classes of data seem to be different from each other. It is expected to bemore complicated due to the number of data points of clusters in each class also be different. Traditional algorithmsas Support Vector Machine (SVM), Twin Support Vector Machine (TSVM), or Least Square Twin Support VectorMachine (LSTSVM) cannot sufficiently exploit information about the number of data points in each cluster of the data.Which may be effect to the accuracy of classification problems. In this paper, we propose a new Improvement LeastSquare - Support Vector Machine (called ILS-SVM) for binary classification problems with a class-vs-clusters strategy.Experimental results show that the ILS-SVM training time is faster than that of TSVM, and the ILS-SVM accuracy isbetter than LSTSVM and TSVM in most cases.

An Improvement Of Least Square - Twin Support Vector Machine

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Thanh Vi Nguyen ◽  
Thế Cường Nguyễn
Keyword(s):  
Support Vector Machine ◽  
Binary Classification ◽  
Least Square ◽  
Experimental Results ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Training Time ◽  
Data Points ◽  
Better Than

n binary classification problems, two classes of data seem tobe different from each other. It is expected to be more complicated dueto the number of data points of clusters in each class also be different.Traditional algorithms as Support Vector Machine (SVM), Twin Support Vector Machine (TSVM), or Least Square Twin Support VectorMachine (LSTSVM) cannot sufficiently exploit information about thenumber of data points in each cluster of the data. Which may be effectto the accuracy of classification problems. In this paper, we proposes anew Improved Least Square - Support Vector Machine (called ILS-SVM)for binary classification problems with a class-vs-clusters strategy. Experimental results show that the ILS-SVM training time is faster thanthat of TSVM, and the ILS-SVM accuracy is better than LSTSVM andTSVM in most cases.

L1-norm loss-based projection twin support vector machine for binary classification

2019 ◽  
Vol 23 (21) ◽  
pp. 10649-10659 ◽  
Author(s):  
Xiaopeng Hua ◽  
Sen Xu ◽  
Jun Gao ◽  
Shifei Ding
Keyword(s):  
Support Vector Machine ◽  
Binary Classification ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
L1 Norm

scholarly journals Hierarchical Multi Twin Support Vector Machine

2021 ◽  
Vol 130 (2B) ◽  
Author(s):  
Nguyen The Cuong
Keyword(s):  
Support Vector Machine ◽  
Structural Information ◽  
Binary Classification ◽  
Classification Problem ◽  
Data Type ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Data Simulation ◽  
Better Than

In binary classification problems, two classes normally have different tendencies. More complex, the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) don't sufficiently exploit structural information with cluster granularity of the data, cause of restricts the capability of simulation of data trends. Structural twin support vector machine (S-TWSVM) sufficiently exploits structural information with cluster granularity of one class for learning a represented hyperplane of that class. This makes S-TWSVM's data simulation capabilities better than TWSVM. However, for the data type that each class consists of clusters of different trends, the capability of simulation of S-TWSVM is restricted. In this paper, we propose a new Hierarchical Multi Twin Support Vector Machine (called HM-TWSVM) for classification problem with each cluster-vs-class strategy. HM-TWSVM overcomes the limitations of S-TWSVM. Experiment results show that HM-TWSVM could describe the tendency of each cluster.

scholarly journals Phishing Website Classification using Least Square Twin Support Vector Machine

2019 ◽  
Vol 9 (1) ◽  
pp. 2063-2068
Keyword(s):  
Support Vector Machine ◽  
Cyber Security ◽  
Classification Accuracy ◽  
Personal Information ◽  
Classification Problem ◽  
Least Square ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Security Issue

Phishing is one among the luring procedures used by phishing attackers in the means to abuse the personal details of clients. Phishing is earnest cyber security issue that includes facsimileing legitimate website to apostatize online users so as to purloin their personal information. Phishing can be viewed as special type of classification problem where the classifier is built from substantial number of website's features. It is required to identify the best features for improving classifiers accuracy. This study, highlights on the important features of websites that are used to classify the phishing website and form the legitimate ones by presenting a scheme Decision Tree Least Square Twin Support Vector Machine (DT-LST-SVM) for the classification of phishing website. UCI public domain benchmark website phishing dataset was used to conduct the experiment on the proposed classifier with different kernel function and calculate the classification accuracy of the classifiers. Computational results show that DT-LST-SVM scheme yield the better classification accuracy with phishing websites classification dataset

scholarly journals WEIGHTED STRUCTURAL SUPPORT VECTOR MACHINE

2021 ◽  
Vol 37 (1) ◽  
pp. 43-56
Author(s):  
Nguyen The Cuong ◽  
Huynh The Phung
Keyword(s):  
Support Vector Machine ◽  
Structural Information ◽  
Binary Classification ◽  
Classification Problem ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Data Simulation ◽  
Training Time ◽  
Structural Support

In binary classification problems, two classes of data seem to be different from each other. It is expected to be more complicated due to the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) cannot sufficiently exploit structural information with cluster granularity of the data, cause limitation on the capability of simulation of data trends. Structural Twin Support Vector Machine (S-TWSVM) sufficiently exploits structural information with cluster granularity for learning a represented hyperplane. Therefore, the capability of S-TWSVM’s data simulation is better than that of TWSVM. However, for the datasets where each class consists of clusters of different trends, the S-TWSVM’s data simulation capability seems restricted. Besides, the training time of S-TWSVM has not been improved compared to TWSVM. This paper proposes a new Weighted Structural - Support Vector Machine (called WS-SVM) for binary classification problems with a class-vs-clusters strategy. Experimental results show that WS-SVM could describe the tendency of the distribution of cluster information. Furthermore, both the theory and experiment show that the training time of the WS-SVM for classification problem has significantly improved compared to S-TWSVM.

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