Research on cost-sensitive learning in one-class anomaly detection algorithms
作者： Jun Luo , Li Ding , Zhisong Pan , Guiqiang Ni , Guyu Hu
DOI: 10.1007/978-3-540-73547-2_27
关键词:
摘要: 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 Frequency-Based SVDD (F-SVDD) Model while Input data division method used Write-Related (W-SVDD) Model. Experimental results show that both of new models have a low false positive rate compared with traditional one. The true positives increased by 22% and 23% False Positives decreased 58% 94%, which reaches nearly 100% 0% respectively. And hence, adjusting some parameters can make better. So Problems may be future orientation Trusted Computing area.
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