TY - JOUR A2 - Dabnichki, Peter AU - Li, Peng AU - Liang, Tian-ge AU - Zhang, Kai-hui PY - 2016 DA - 2016/07/31 TI - Imbalanced Data Set CSVM Classification Method Based on Cluster Boundary Sampling SP - 1540628 VL - 2016 AB - 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. SN - 1024-123X UR - https://doi.org/10.1155/2016/1540628 DO - 10.1155/2016/1540628 JF - Mathematical Problems in Engineering PB - Hindawi Publishing Corporation KW - ER -