Ordenación de lotes basada en la confianza en el aprendizaje continuo: un enfoque de aprendizaje curricular para...

Training machine learning models on large datasets, such as those derived from single-cell RNA sequencing (scRNA-seq), poses significant challenges due to high computational and memory demands. Additionally, integrating data from diverse sources introduces complexities stemming from experimental variability, technological differences, and data privacy concerns. Continual learning (CL) algorithms offer a promising solution by incrementally training models on data batches while addressing issues like catastrophic forgetting. However, the sequence in which these batches are presented has been largely overlooked despite its potential to influence learning efficiency and model performance. This study introduces a confidence-based batch ordering strategy for CL algorithms, leveraging confidence estimation to prioritize training samples. By structuring batches in ascending order of confidence, we observed consistent improvements in classification performance across multiple scRNA-seq datasets. Specifically, intra-dataset experiments revealed that ascending confidence ordering consistently outperformed random or descending orderings in terms of median F1 scores, highlighting its efficacy in enhancing model generalization. Similarly, inter-dataset analyses demonstrated that confidence-based ordering improved robustness when training on heterogeneous datasets generated using different sequencing protocols. Our findings highlight the critical role of batch sequencing in optimizing CL workflows for data-intensive tasks. Future research may explore the extension of this strategy to other domains and investigate adaptive confidence metrics tailored to dynamic datasets.