Binary classification with noise via fuzzy weighted least squares twin support vector machine

2015 ◽  
Author(s):  
Juntao Li ◽  
Yimin Cao ◽  
Yadi Wang ◽  
Xiaoxia Mu ◽  
Liuyuan Chen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Binary Classification ◽  
Weighted Least Squares ◽  
Twin Support Vector Machine ◽  
Support Vector

scholarly journals Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Divya Tomar ◽  
Sonali Agarwal
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Geometric Mean ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Diagnostic Model ◽  
Feature Subset

There is a necessity for analysis of a large amount of data in many fields such as healthcare, business, industries, and agriculture. Therefore, the need of the feature selection (FS) technique for the researchers is quite evident in many fields of science, especially in computer science. Furthermore, an effective FS technique that is best suited to a particular learning algorithm is of great help for the researchers. Hence, this paper proposes a hybrid feature selection (HFS) based efficient disease diagnostic model for Breast Cancer, Hepatitis, and Diabetes. A HFS is an efficient method that combines the positive aspects of both Filter and Wrapper FS approaches. The proposed model adopts weighted least squares twin support vector machine (WLSTSVM) as a classification approach, sequential forward selection (SFS) as a search strategy, and correlation feature selection (CFS) to evaluate the importance of each feature. This model not only selects relevant feature subset but also efficiently deals with the data imbalance problem. The effectiveness of the HFS based WLSTSVM approach is examined on three well-known disease datasets taken from UCI repository with the help of predictive accuracy, sensitivity, specificity, and geometric mean. The experiment confirms that our proposed HFS based WLSTSVM disease diagnostic model can result in positive outcomes.

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

Variable-weighted least-squares support vector machine for multivariate spectral analysis

Talanta ◽  
2010 ◽  
Vol 80 (5) ◽  
pp. 1698-1701 ◽  
Author(s):  
Hong-Yan Zou ◽  
Hai-Long Wu ◽  
Hai-Yan Fu ◽  
Li-Juan Tang ◽  
Lu Xu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Spectral Analysis ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Support Vector

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 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.

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