Work

SAB: Self-Adaptive Bias

Year: 2024

Type: article

Abstract: Curriculum learning is a method of prioritizing learning data to improve learning performance. In this paper, we propose a new algorithm that determines how to select learning data and when to start and stop curriculum learning by considering learning errors. We use entropy to select data samples with less consistent predictions and automatically determine the warming-up period based on the characteristics of the data. Additionally, to mitigate learning bias, we introduced a variable that adjusts the range of sample selection according to the progress of the training. To validate our method, we conducted extensive experiments on both balanced and imbalanced data classification tasks, and our proposed approach showed an average improvement of about 1.8%, with a maximum improvement of up to 4.4%, compared to previously suggested methods.

Source: AI

DOI: 10.3390/ai5040133

Authors: Suchan Choi, Jinyoung Oh, Jeong-Won Cha

Institution: Changwon National University

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $1,819