Siamese networks with an online reweighted example for imbalanced data learning

Linchang ZHAO*, Zhaowei SHANG, Jin TAN, Mingliang ZHOU, Mu ZHANG, Dagang GU, Taiping ZHANG, Yuan Yan TANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

7 Citations (Scopus)

Abstract

One key challenging problem in data mining and decision-making is to establish a decision support system based on unbalanced datasets. In this study, we propose a novel algorithm to handle unbalanced learning problems that integrates the advantages of Siamese convolutional neural networks (SCNN) and the online reweighted example (ORE) algorithm into a unified method. First, the SCNN model is established for learning and extracting deep feature features at different levels. Second, the ORE algorithm is used to address the problem of data with a class-imbalanced distribution. Compared with baseline approaches, the experimental results show that our proposed method substantially enhances the performance of both within-project defect prediction and cross-project defect prediction.

Original languageEnglish
Article number108947
Number of pages10
JournalPattern Recognition
Volume132
Early online date26 Jul 2022
DOIs
Publication statusPublished - Dec 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • Data mining
  • Few-shot learning
  • Imbalanced learning
  • Reweighted example learning

Fingerprint

Dive into the research topics of 'Siamese networks with an online reweighted example for imbalanced data learning'. Together they form a unique fingerprint.

Cite this