 poses the important challenge of adapting to evolving data distributions without forgetting previously acquired knowledge while consolidating new knowledge. In this paper%2C we introduce a new methodology%2C coined as Tabular-data Rehearsal-based Incremental Lifelong Learning framework (TRIL3)%2C 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%2C which was modified to run in an incremental way%2C to learn classification tasks for tabular data%2C without storing old samples. After different tests to obtain the adequate percentage of synthetic data and to compare TRIL3 with other CL available proposals%2C we can conclude that the performance of TRIL3 outstands other options in the literature using only 50%25 of synthetic data.?quality=80&w=1200)
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