  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.
