Overcoming catastrophic forgetting in tabular data classification: A pseudorehearsal-based approach

IF 8 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Pablo García-Santaclara , Bruno Fernández-Castro , Rebeca P. Díaz-Redondo
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引用次数: 0

Abstract

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 the Tabular-data Rehearsal-based Incremental Lifelong Learning framework (TRIL3), designed to address the phenomenon of catastrophic forgetting in an online, task-free setting. TRIL3 utilizes an Incremental Learning Vector Quantization (ILVQ) algorithm as an efficient prototype-based incremental generative model to store and generate synthetic data to preserve knowledge over time, and the Deep Neural Decision Forest (DNDF) algorithm, which was modified to run incrementally to learn supervised classification tasks for tabular data. Based on tests conducted to determine the optimal percentage of synthetic data and comparisons with other available task-free CL proposals, we conclude that TRIL3 outperforms other methods in the literature using only 50% of synthetic data.
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
持续学习(CL)提出了重要的挑战,即在巩固新知识的同时不忘记以前获得的知识,以适应不断变化的数据分布。在本文中,我们介绍了一种新的方法,即基于表格数据预演的增量终身学习框架(TRIL3),旨在解决在线、无任务环境下的灾难性遗忘现象。TRIL3利用增量学习向量量化(ILVQ)算法作为一种高效的基于原型的增量生成模型来存储和生成合成数据,以随着时间的推移保存知识,并利用深度神经决策森林(DNDF)算法,该算法经过修改以增量方式运行,以学习表格数据的监督分类任务。基于为确定合成数据的最佳百分比而进行的测试以及与其他可用的无任务CL提案的比较,我们得出结论,TRIL3仅使用50%的合成数据就优于文献中的其他方法。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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