eProceedings of Engineering
Vol 10, No 5 (2023): Oktober 2023

Performance Analysis Of Class Rebalancing Self-Training Framework For Imbalanced Semi-Supervised Learning

Alvaro Septra Dominggo Nauw (Telkom University)
Suryo Adhi Wibowo (Telkom University)
Casi Setianingsih (Telkom University)

Article Info

Publish Date
01 Nov 2023

Abstract

This research aims to analyze the effectiveness ofthe Class-Rebalancing Self-Training (CReST) method in semisupervisedlearning (SSL) on class-imbalanced data. The studyuses the CIFAR 10 long-tailed dataset to test the performance ofSSL with CReST using Python programming language on theGoogle Colab platform. The results showed that CReSTeffectively reduces pseudo-labels in the majority class andincreases recall in the minority class, with the best performanceachieved at Generation 16. However, there was a decrease inAverage Accuracy Recall per Class after Generation 16. Thestudy suggests addressing the over-sampling issue and exploringthe application of the CReST framework in other areas ofmachine learning and AI.Kata kunci— CReST, Semi-Supervised Learning, imbalancedata, pseudo label, Semi-Supervised Learning Generation

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Journal Info
eProceedings of Engineering
Website

Abbrev

engineering

Publisher

Universitas Telkom

Subject

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Description

Merupakan media publikasi karya ilmiah lulusan Universitas Telkom yang berisi tentang kajian teknik. Karya Tulis ilmiah yang diunggah akan melalui prosedur pemeriksaan (reviewer) dan approval pembimbing ...