A Dynamic Cost Sensitive Support Vector Machine |
|
| Journal | Advanced Materials Research (Volumes 424 - 425) |
|---|---|
| Volume | Advanced Research on Engineering Materials, Energy, Management and Control |
| Edited by | Helen Zhang and David Jin |
| Pages | 1342-1346 |
| DOI | 10.4028/www.scientific.net/AMR.424-425.1342 |
| Citation | Xiao Lin Chen et al., 2012, Advanced Materials Research, 424-425, 1342 |
| Online since | January, 2012 |
| Authors | Xiao Lin Chen, Yan Jiang, Min Jie Chen, Yong Yu, Hong Ping Nie, Min Li |
| Keywords | Genetic Algorithm (GA), Misclassification Costs, Pattern Recognition, Support Vector Machine (SVM) |
| Abstract | 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. |
| Full Paper | 
Get the full paper by clicking here
|
First page example
