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.
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.