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A Semi-supervised Resampling Method for Class-imbalanced Learning by Zhen Jiang; Lingyun Zhao; Yu Lu; Yongzhao Zhan; Qirong Mao is a Computer Science article available to read on EtoBox.

What is A Semi-supervised Resampling Method for Class-imbalanced Learning about?

Clustering analysis is widely used as a pre-process to discover the data distribution for resampling. Existing clustering-based resampling methods mostly run unsupervised clustering on labeled data without taking advantage of the class information to guide the clustering. When there are not enough labeled data, the clustering can hardly capture the underlying data distribution. In this paper, we propose a semi-supervised hybrid resampling (SSHR) method which runs semi-supervised clustering to capture the data distribution for both over-sampling and under-sampling. Firstly, we design a semi-supervised hierarchical clustering algorithm (SSHC) which uses labeled data to guide the clustering procedure on the whole dataset. Specifically, labeled data are used to initialize a clustering model and then guide its updating via an iterative cluster-splitting process. In this way, original classes are divided into multiple disjunct clusters, which contributes o disclosing not only the inter-class imbalance but also the intra-class imbalance. Subsequently, a hybrid resampling is performed according to the result of SSHC Labeled data of the majority class are under-sampled according to the dist

Who reads A Semi-supervised Resampling Method for Class-imbalanced Learning?

Author

Zhen Jiang; Lingyun Zhao; Yu Lu; Yongzhao Zhan; Qirong Mao

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)