TY - JOUR
T1 - Unbalanced Class-incremental Learning for Text Classification Based on Experience Replay
AU - Chen, Lifeng
AU - Zhang, Huaping
AU - Wushour, Silamu
AU - Li, Yugang
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2023
Y1 - 2023
N2 - While deep learning has achieved remarkable results for text classification, incremental learning for text classification is still a challenge. The main problem is that models suffer from catastrophic forgetting, which is they always forget knowledge learned before when labelled data comes sequentially and is trained in sequence. In this study, we propose methods of preventing catastrophic forgetting to handle unbalanced increased data. As an improvement over experience replay, our approaches improve the accuracy about 23.3% with 23% of all training data on Yahoo and 9.5% with 12% of all training data and on DBPedia.
AB - While deep learning has achieved remarkable results for text classification, incremental learning for text classification is still a challenge. The main problem is that models suffer from catastrophic forgetting, which is they always forget knowledge learned before when labelled data comes sequentially and is trained in sequence. In this study, we propose methods of preventing catastrophic forgetting to handle unbalanced increased data. As an improvement over experience replay, our approaches improve the accuracy about 23.3% with 23% of all training data on Yahoo and 9.5% with 12% of all training data and on DBPedia.
UR - http://www.scopus.com/inward/record.url?scp=85166679392&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/2513/1/012001
DO - 10.1088/1742-6596/2513/1/012001
M3 - Conference article
AN - SCOPUS:85166679392
SN - 1742-6588
VL - 2513
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012001
T2 - 2023 7th International Conference on Artificial Intelligence, Automation and Control Technologies, AIACT 2023
Y2 - 24 February 2023 through 26 February 2023
ER -