Affiliations: [a] College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China | [b] Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning, China | [c] Computing Science, University of Alberta, Edmonton, Alberta, Canada
Correspondence:
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Corresponding author: Peng Cao, Neusoft Research Institute, No 2 Xinxiu Street, Shenyang 110179, Liaoning, China. Tel.: +86 24 8366 5404; Fax: +86 24 8366 3446; E-mail: [email protected].
Abstract: Class imbalance is one of the challenging problems for machine learning in many real-world applications. Other issues, such as within-class imbalance and high dimensionality, can exacerbate the problem. We propose a method HPS-DRS that combines two ideas: Hybrid Probabilistic Sampling technique ensemble with Diverse Random Subspace to address these issues. HPS improves the performance of traditional re-sampling algorithms with the aid of probability function, since it is not sufficient to simply manipulate the class sizes for imbalanced data with complex distribution. Moreover, DRS ensemble employs the minimum overlapping mechanism to provide diversity and weighted voting, so as to improve the generalization performance. The experimental results demonstrate that our method is efficient for learning from imbalanced data and can achieve better results than state-of-the-art methods for imbalanced data.
Keywords: Classification, class imbalance, sampling method, ensemble learning, random subspace method
