MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||6|
|Section||Modelling and Simulation|
|Published online||04 March 2020|
Feature selected cost-sensitive twin SVM for imbalanced data
1 Zhejiang normal university, jinhua city, zhejiang province, China
2 Keyuan new village, jinzhai road, baohe district, hefei city, anhui province, China
* Corresponding author: firstname.lastname@example.org
In this paper, we propose a cost-sensitive twin SVM (cs-tsvm) and apply it to imbalanced data. A weight is added to each instance according to its cost of misclassification which is related to its position. In preprocessing part, features are selected by their difference of majority and minority classes. The feature is selected when its difference value is higher than average one. The experiment is conducted on UCI datasets and G-mean, AUC and accuracy are evaluation metrics. The experimental results show that Feature selection with CS-TWSVM is useful for datasets with high dimension.
Key words: Twin SVM / cost-sensitive / feature selection / imbalanced data
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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