According to the Cost-Sensitive Learning Method, two improved One-Class Anomaly Detection Models using Support Vector Data Description (SVDD) are put forward in this paper. Improved Algorithm is included in the Frequency-Based SVDD (F-SVDD) Model while Input data division method is used in the Write-Related SVDD (W-SVDD) Model. Experimental results show that both of the two new models have a low false positive rate compared with the traditional one. The true positives increased by 22% and 23% while the False Positives decreased by 58% and 94%, which reaches nearly 100% and 0% respectively. And hence, adjusting some parameters can make the false positive rate better. So using Cost-Sensitive method in One-Class Problems may be a future orientation in Trusted Computing area. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Luo, J., Ding, L., Pan, Z., Ni, G., & Hu, G. (2007). Research on cost-sensitive learning in one-class anomaly detection algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4610 LNCS, pp. 259–268). Springer Verlag. https://doi.org/10.1007/978-3-540-73547-2_27
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