PDF(Pubmed)
Abstract：
UNASSIGNED: This study examines the performance of active learning-aided systematic reviews using a deep learning-based model compared to traditional machine learning approaches, and explores the potential benefits of model-switching strategies.
UNASSIGNED: Comprising four parts, the study: 1) analyzes the performance and stability of active learning-aided systematic review; 2) implements a convolutional neural network classifier; 3) compares classifier and feature extractor performance; and 4) investigates the impact of model-switching strategies on review performance.
UNASSIGNED: Lighter models perform well in early simulation stages, while other models show increased performance in later stages. Model-switching strategies generally improve performance compared to using the default classification model alone.
UNASSIGNED: The study\'s findings support the use of model-switching strategies in active learning-based systematic review workflows. It is advised to begin the review with a light model, such as Naïve Bayes or logistic regression, and switch to a heavier classification model based on a heuristic rule when needed.
UNASSIGNED: Comprising four parts, the study: 1) analyzes the performance and stability of active learning-aided systematic review; 2) implements a convolutional neural network classifier; 3) compares classifier and feature extractor performance; and 4) investigates the impact of model-switching strategies on review performance.
UNASSIGNED: Lighter models perform well in early simulation stages, while other models show increased performance in later stages. Model-switching strategies generally improve performance compared to using the default classification model alone.
UNASSIGNED: The study\'s findings support the use of model-switching strategies in active learning-based systematic review workflows. It is advised to begin the review with a light model, such as Naïve Bayes or logistic regression, and switch to a heavier classification model based on a heuristic rule when needed.
摘要：
■与传统的机器学习方法相比，本研究检查了使用基于深度学习的模型的主动学习辅助系统评论的性能。并探讨了模型转换策略的潜在好处。
■由四个部分组成，研究：1）分析主动学习辅助系统综述的性能和稳定性；2）实现卷积神经网络分类器；3）比较分类器和特征提取器的性能；4）研究模型切换策略对综述性能的影响。
■打火机模型在早期模拟阶段表现良好，而其他型号在后期显示出更高的性能。与单独使用默认分类模型相比，模型切换策略通常会提高性能。
■研究结果支持在基于主动学习的系统综述工作流程中使用模型转换策略。建议以轻型模型开始审查，如朴素贝叶斯或逻辑回归，并在需要时基于启发式规则切换到较重的分类模型。
■由四个部分组成，研究：1）分析主动学习辅助系统综述的性能和稳定性；2）实现卷积神经网络分类器；3）比较分类器和特征提取器的性能；4）研究模型切换策略对综述性能的影响。
■打火机模型在早期模拟阶段表现良好，而其他型号在后期显示出更高的性能。与单独使用默认分类模型相比，模型切换策略通常会提高性能。
■研究结果支持在基于主动学习的系统综述工作流程中使用模型转换策略。建议以轻型模型开始审查，如朴素贝叶斯或逻辑回归，并在需要时基于启发式规则切换到较重的分类模型。
