A genetic algorithm to optimize SMOTE and GAN ratios in class imbalanced datasets

2020 ◽  
Author(s):  
Hwi-Yeon Cho ◽  
Yong-Hyuk Kim
Keyword(s):  
Genetic Algorithm ◽  
Imbalanced Datasets

A Dynamic Cost Sensitive Support Vector Machine

2012 ◽  
Vol 424-425 ◽  
pp. 1342-1346 ◽  
Author(s):  
Xiao Lin Chen ◽  
Yan Jiang ◽  
Min Jie Chen ◽  
Yong Yu ◽  
Hong Ping Nie ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Experimental Results ◽  
Promising Method ◽  
The Other ◽  
Support Vector ◽  
Imbalanced Datasets ◽  
Misclassification Cost

A lot of cost-sensitive support machine vector methods are used to handle the imbalanced datasets, but the obtained results are not as perfect as expectation. A promising method is proposed in this paper, named ADC-SVM, which uses genetic algorithm to dynamically search the optimal misclassification cost to build a cost sensitive support machine. We empirically evaluate ADC-SVM with SVM and Cost-sensitive SVM over 8 realistic imbalanced bi-class datasets from UCI. The experimental results show that ADC-SVM outperforms the other two methods over all the imbalanced datasets.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy
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