scholarly journals Weighted Least Square Twin Support Vector Machine for Imbalanced Dataset

2014 ◽  
Vol 7 (2) ◽  
pp. 25-36 ◽  
Author(s):  
Divya Tomar ◽  
Shubham Singhal ◽  
Sonali Agarwal
Keyword(s):  
Support Vector Machine ◽  
Least Square ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Imbalanced Dataset ◽  
Weighted Least Square

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.

scholarly journals Icing Forecasting of High Voltage Transmission Line Using Weighted Least Square Support Vector Machine with Fireworks Algorithm for Feature Selection

2016 ◽  
Author(s):  
Tiannan Ma ◽  
Dongxiao Niu
Keyword(s):  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Data Set ◽  
Promising Alternative ◽  
Forecasting Accuracy ◽  
High Voltage Transmission Line ◽  
Fireworks Algorithm ◽  
Weighted Least Square ◽  
Forecasting System

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

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

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.

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