A novel oversampling technique based on the manifold distance for class imbalance learning

Date

2020-11-25

Advisors

Journal Title

Journal ISSN

ISSN

1758-0366

Volume Title

Publisher

Inderscience Publishers Ltd

Type

Article

Peer reviewed

Yes

Abstract

Oversampling is a popular problem-solver for class imbalance learning by generating more minority samples to balance the dataset size of different classes. However, resampling in original space is ineffective for the imbalance datasets with class overlapping or small disjunction. Based on this, a novel oversampling technique based on manifold distance is proposed, in which a new minority sample is produced in terms of the distances among neighbors in manifold space, rather than the Euclidean distance among them. After mapping the original data to its manifold structure, the overlapped majority and minority samples will lie in areas easily being partitioned. In addition, the new samples are generated based on the neighbors locating nearby in manifold space, avoiding the adverse effect of the disjoint minority classes. Following that, an adaptive adjustment method is presented to determine the number of the newly generated minority samples according to the distribution density of the matched-pair data. The experimental results on 48 imbalanced datasets indicate that the proposed oversampling technique has the better classification accuracy.

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

Class imbalance learning, oversampling, manifold learning, overlapping, small disjunction

Citation

Guo, Y., Jiao, B., Cheng, J., Yang, L., Yang, S., and Tang, F. (2020) A novel oversampling technique based on the manifold distance for class imbalance learning. International Journal of Bio-Inspired Computation, 18 (3), pp. 131-142

Rights

Research Institute

Institute of Artificial Intelligence (IAI)