Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling
Joint Authors
Li, Peng
Liang, Tian-ge
Zhang, Kai-hui
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-07-31
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper creatively proposes a cluster boundary sampling method based on density clustering to solve the problem of resampling in IDS classification and verify its effectiveness experimentally.
We use the clustering density threshold and the boundary density threshold to determine the cluster boundaries, in order to guide the process of resampling more scientifically and accurately.
Then, we adopt the penalty factor to regulate the data imbalance effect on SVM classification algorithm.
The achievements and scientific significance of this paper do not propose the best classifier or solution of imbalanced data set and just verify the validity and stability of proposed IDS resampling method.
Experiments show that our method acquires obvious promotion effect in various imbalanced data sets.
American Psychological Association (APA)
Li, Peng& Liang, Tian-ge& Zhang, Kai-hui. 2016. Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1111741
Modern Language Association (MLA)
Li, Peng…[et al.]. Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling. Mathematical Problems in Engineering No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1111741
American Medical Association (AMA)
Li, Peng& Liang, Tian-ge& Zhang, Kai-hui. Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1111741
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111741