克服表格数据分类中的灾难性遗忘：一种基于伪排练的方法
Overcoming Catastrophic Forgetting in Tabular Data Classification: A Pseudorehearsal-based approach
AI总结：
针对表格数据分类的灾难性遗忘问题，提出基于伪排练的TRIL3框架，结合XuILVQ生成合成数据与增量式DNDF算法，无需存储旧样本，仅用50%合成数据性能就优于现有持续学习方案。
摘要：
持续学习（CL）面临的重要挑战是适应不断演变的数据分布，同时在巩固新知识的同时不遗忘先前习得的知识。本文提出了一种名为Tabular-data Rehearsal-based Incremental Lifelong Learning framework（TRIL3，基于表格数据排练的增量终身学习框架）的新方法，旨在解决表格数据分类问题中的灾难性遗忘现象。TRIL3采用基于原型的增量生成模型XuILVQ生成合成数据以保留旧知识，并使用经改进可增量运行的DNDF算法来学习表格数据的分类任务，无需存储旧样本。经过多次测试以确定合适的合成数据比例，并将TRIL3与其他现有持续学习方案进行对比后，我们可以得出结论：仅使用50%的合成数据，TRIL3的性能就优于现有文献中的其他方案。
英文摘要：
Continual learning (CL) poses the important challenge of adapting to evolving data distributions without forgetting previously acquired knowledge while consolidating new knowledge. In this paper, we introduce a new methodology, coined as Tabular-data Rehearsal-based Incremental Lifelong Learning framework (TRIL3), designed to address the phenomenon of catastrophic forgetting in tabular data classification problems. TRIL3 uses the prototype-based incremental generative model XuILVQ to generate synthetic data to preserve old knowledge and the DNDF algorithm, which was modified to run in an incremental way, to learn classification tasks for tabular data, without storing old samples. After different tests to obtain the adequate percentage of synthetic data and to compare TRIL3 with other CL available proposals, we can conclude that the performance of TRIL3 outstands other options in the literature using only 50% of synthetic data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
