DOAJ Open Access 2020

Feature selected cost-sensitive twin SVM for imbalanced data

Li Xiaopeng Zhang Xianrong

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Engineering (General). Civil engineering (General)

Penulis (2)

Li Xiaopeng

Zhang Xianrong

Format Sitasi

APA MLA BibTeX

Xiaopeng, L., Xianrong, Z. (2020). Feature selected cost-sensitive twin SVM for imbalanced data. https://doi.org/10.1051/matecconf/202030905013

Akses Cepat

Lihat di Sumber doi.org/10.1051/matecconf/202030905013

Informasi Jurnal

Tahun Terbit: 2020
Sumber Database: DOAJ
DOI: 10.1051/matecconf/202030905013
Akses: Open Access ✓