Dynamic Nonparametric Random Forest Using Covariance
المؤلفون المشاركون
Choi, Seok-Hwan
Shin, Jin-Myeong
Choi, Yoon-Ho
المصدر
Security and Communication Networks
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-03-27
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
As the representative ensemble machine learning method, the Random Forest (RF) algorithm has widely been used in diverse applications on behalf of the fast learning speed and the high classification accuracy.
Research on RF can be classified into two categories: (1) improving the classification accuracy and (2) decreasing the number of trees in a forest.
However, most of papers related to the performance improvement of RF have focused on improving the classification accuracy.
Only some papers have focused on reducing the number of trees in a forest.
In this paper, we propose a new Covariance-Based Dynamic RF algorithm, called C-DRF.
Compared to the previous works, while ensuring the good-enough classification accuracy, the proposed C-DRF algorithm reduces the number of trees.
Specifically, by computing the covariance between the number of trees in a forest and F-measure at each iteration, the proposed algorithm determines whether to increase the number of trees composing a forest.
To evaluate the performance of the proposed C-DRF algorithm, we compared the learning time, the test time, and the memory usage with the original RF algorithm under the different areas of datasets.
Under the same or higher classification accuracy, it is shown that the proposed C-DRF algorithm improves the performance of the original RF algorithm by as much as 58.68% at learning time, 47.91% at test time, and 68.06% in memory usage on average.
As a practical application area, we also show that the proposed C-DRF algorithm is more efficient than the state-of-the-art RF algorithms in Network Intrusion Detection (NID) area.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Choi, Seok-Hwan& Shin, Jin-Myeong& Choi, Yoon-Ho. 2019. Dynamic Nonparametric Random Forest Using Covariance. Security and Communication Networks،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1210409
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Choi, Seok-Hwan…[et al.]. Dynamic Nonparametric Random Forest Using Covariance. Security and Communication Networks No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1210409
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Choi, Seok-Hwan& Shin, Jin-Myeong& Choi, Yoon-Ho. Dynamic Nonparametric Random Forest Using Covariance. Security and Communication Networks. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1210409
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1210409
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر