Paper Info
Title
A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM.
Abstract
Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model. Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones. However, the samples near the decision boundary which contain more discriminative information should be valued and the skew of the boundary would be corrected by constructing synthetic samples. Inspired by the truth and sense of geometry, we designed a new synthetic minority oversampling technique to incorporate the borderline information. What is more, ensemble model always tends to capture more complicated and robust decision boundary in practice. Taking these factors into considerations, a novel ensemble method, called Bagging of Extrapolation Borderline-SMOTE SVM (BEBS), has been proposed in dealing with imbalanced data learning (IDL) problems. Experiments on open access datasets showed significant superior performance using our model and a persuasive and intuitive explanation behind the method was illustrated. As far as we know, this is the first model combining ensemble of SVMs with borderline information for solving such condition.
Year
DOI
Venue
2017
10.1155/2017/1827016
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
DocType
Volume
ISSN
Journal
2017
1687-5265
Citations 
PageRank 
References 
7
0.54
0
Authors
5
Name
Order
Citations
PageRank
Qi Wang1111.67
ZhiHao Luo280.89
Jincai Huang35416.88
Yang-He Feng4239.91
Zhong Liu5111.04