کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6856951 | 1437972 | 2018 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Incremental anomaly detection using two-layer cluster-based structure
ترجمه فارسی عنوان
تشخیص اضافه شدن آنومالی با استفاده از ساختار مبتنی بر خوشه ای دو لایه
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
The proposed method is found to lower the false alarm rate, which is one of the basic problems for the one-class SVM. Experiments show the false alarm rate is decreased from 5% to 15% among different datasets, while the detection rate is increased from 5% to 10% in different datasets with two-layer structure. The memory usage for the two-layer structure is 20 to 50 times less than that of one-class SVM. The one-class SVM uses support vectors in labeling new instances, while the labeling of the two-layer structure depends on the number of GMMs. The experiments show that the two-layer structure is 20 to 50 times faster than the one-class SVM in labeling new instances. Moreover, the updating time of the two-layer structure is two to three times less than for a one-layer structure. This reduction is the result of using two-layer structure and ignoring redundant instances.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 429, March 2018, Pages 315-331
Journal: Information Sciences - Volume 429, March 2018, Pages 315-331
نویسندگان
Elnaz Bigdeli, Mahdi Mohammadi, Bijan Raahemi, Stan Matwin,