A Novel Differential Evolution-Clustering Hybrid Resampling Algorithm on Imbalanced Datasets

  title={A Novel Differential Evolution-Clustering Hybrid Resampling Algorithm on Imbalanced Datasets},
  author={Leichen Chen and Zhihua Cai and Lu Chen and Qiong Gu},
  journal={2010 Third International Conference on Knowledge Discovery and Data Mining},
When dealing with the imbalanced datasets (IDS), the hyperplane of Support vector machine (SVM) tends to minority class (positive class), which causes low classification accuracy. Aiming at this problem, we propose a novel differential evolution-clustering hybrid resampling SVM algorithm (DEC-SVM). This algorithm utilizes the similar mutation and crossover operators of Differential Evolution (DE) for over-sampling to enlarge the ratio of positive samples, and then we apply clustering to the… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.


Publications referenced by this paper.
Showing 1-10 of 22 references

A Hybrid Re-sampling Method for SVM Learning from Imbalanced Data Sets

2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery • 2008

Imbalance Data Set Classification Using SMOTE and Biased-SVM

Heyong Wang, Hongkun Fan, Zheng’an Yao
Computer Science. Vol. 35(5):174-176, • 2008

A New Support Vector Machine Method for Unbalanced Data Treatment

Hongxing Wu, Yu Peng, Xiyuan Peng
Acta Electronica Sinica • 2006

Pruning support vectors for imbalanced data classification

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. • 2005
View 2 Excerpts

A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data

E. A. Gustavo P.A. Batista, C PratiRonaldo, Monard Maria Carolina
ACM SIGKDD Explorations Newsletter • 2004
View 1 Excerpt

Chang . Class - Boundary Alignment for Imbalanced Dataset Learning

Gang Wu, Y. Edward
The ICML Workshop on Learning from Imbalanced Data Sets • 2003

Similar Papers

Loading similar papers…