An ensemble deep learning model for author identification through multiple features
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| Опубликовано в:: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 26477-26491 |
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| Главный автор: | |
| Опубликовано: |
Nature Publishing Group
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| Online-ссылка: | Citation/Abstract Full Text Full Text - PDF |
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| 100 | 1 | |a Zhang, Yuan |u School of Humanities and Social Science, Xi’an Jiaotong University, 710049, Xi’an, Shaanxi Province, China (ROR: https://ror.org/017zhmm22) (GRID: grid.43169.39) (ISNI: 0000 0001 0599 1243); School of Humanities and and Foreign Languages, Xi’an University of Technology, 710049, Xi’an, Shaanxi Province, China (ROR: https://ror.org/038avdt50) (GRID: grid.440722.7) (ISNI: 0000 0000 9591 9677) | |
| 245 | 1 | |a An ensemble deep learning model for author identification through multiple features | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a One of the challenges in the natural language processing is authorship identification. The proposed research will improve the accuracy and stability of authorship identification by creating a new deep learning framework that combines the features of various types in a self-attentive weighted ensemble framework. Our approach enhances generalization to a great extent by combining a wide range of writing styles representations such as statistical features, TF-IDF vectors, and Word2Vec embeddings. The different sets of features are fed through separate Convolutional Neural Networks (CNN) so that the specific stylistic features can be extracted. More importantly, a self-attention mechanism is presented to smartly combine the results of these specialized CNNs so that the model can dynamically learn the significance of each type of features. The summation of the representation is then passed into a weighted SoftMax classifier with the aim of optimizing performance by taking advantage of the strengths of individual branches of the neural network. The suggested model was intensively tested on two different datasets, Dataset A, which included four authors, and Dataset B, which included thirty authors. Our method performed better than the baseline state-of-the-art methods by at least 3.09% and 4.45% on Dataset A and Dataset B respectively with accuracy of 80.29% and 78.44%, respectively. This self-attention-augmented multi-feature ensemble approach is very effective, with significant gains in state-of-the-art accuracy and robustness metrics of author identification. | |
| 653 | |a Machine learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Nonparametric statistics | ||
| 653 | |a Writing | ||
| 653 | |a Regression analysis | ||
| 653 | |a Hypothesis testing | ||
| 653 | |a Data mining | ||
| 653 | |a Identification | ||
| 653 | |a Stylistics | ||
| 653 | |a Linguistics | ||
| 653 | |a Multilingualism | ||
| 653 | |a Natural language processing | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Large language models | ||
| 653 | |a Neural networks | ||
| 653 | |a Information retrieval | ||
| 653 | |a Case studies | ||
| 653 | |a Economic | ||
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 26477-26491 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3231999410/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3231999410/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3231999410/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |