Classification of Imbalanced Data Sets by Using the Hybrid Re-sampling Algorithm Based on Isomap

Authors:
Qiong Gu;Zhihua Cai;Li Zhu
Affiliations:
Faculty of Mathematics & Computer Science, Xiangfan University, Xiangfan, China 441053 and School of Computer, China University of Geosciences, Wuhan, China 430074;School of Computer, China University of Geosciences, Wuhan, China 430074;School of Computer, China University of Geosciences, Wuhan, China 430074
Venue:
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Year:
2009

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Abstract

The majority of machine learning algorithms previously designed usually assume that their training sets are well-balanced, but data in the real-world is usually imbalanced. The class imbalance problem is pervasive and ubiquitous, causing trouble to a large segment of the data mining community. As the conventional machine learning algorithms have bad performance when they learn from imbalanced data sets, it is necessary to find solutions to machine learning on imbalanced data sets. This paper presents a novel Isomap-based hybrid re-sampling approach to improve the conventional SMOTE algorithm by incorporating the Isometric feature mapping algorithm (Isomap). Experiment results demonstrate that this hybrid re-sampling algorithm attains a performance superior to that of the re-sampling. It is clear that the Isomap method is an effective means to reduce the dimension of the re-sampling. This provides a new possible solution for dealing with the IDS classification.