Integrated ensemble of BERT- and feature-based models for authorship attribution in Japanese literary works

BERT demonstrated effectiveness in small-sample authorship attribution tasks, surpassing traditional feature-based methods. Both BERT-based and feature-based ensembles outperformed their standalone counterparts, with the integrated ensemble method achieving even higher scores. Notably, the integrated ensemble significantly outperformed the best individual model on Corpus B-which was not included in the pre-training data- improving the F1 score from 0.823 to 0.96. It achieved the highest score among all evaluated approaches, including standalone models and conventional ensemble techniques, with a statistically significant margin (<i>p</i> < 0.012, Cohen's d = 4.939), underscoring the robustness of the result. The pre-training data used in BERT had a significant impact on task performance, emphasizing the need for careful model selection based not only on accuracy but also on model diversity. These findings highlight the importance of pre-training data and model diversity in optimizing language models for ensemble learning, offering valuable insights for authorship attribution research and the broader development of artificial general intelligence systems.