An Integrated BERT-XGBoost Framework for Open-Source Intelligence Classification in Aerospace Technology
Journal Title: Information Dynamics and Applications - Year 2024, Vol 3, Issue 4
Abstract
Open-source intelligence in aerospace technology often contains lengthy text and numerous technical terms, which can affect classification accuracy. To enhance the precision of classifying such intelligence, a classification algorithm integrating the Bidirectional Encoder Representations from Transformers (BERT) and Extreme Gradient Boosting (XGBoost) models was proposed. Initially, key features within the intelligence were extracted through the deep structure of the BERT model. Subsequently, the XGBoost model was utilised to replace the final output layer of BERT, applying the extracted features for classification. To verify the algorithm's effectiveness, comparative experiments were conducted against prominent language models such as Text Recurrent Convolutional Neural Network (TextRCNN) and Deep Pyramid Convolutional Neural Network (DPCNN). Experimental results demonstrate that, for open-source intelligence classification in aerospace technology, this algorithm achieved accuracy improvements of 1.9% and 2.2% over the TextRCNN and DPCNN models, respectively, confirming the algorithm's efficacy in relevant classification tasks.
Authors and Affiliations
Suping Yu, Weiwei Mao
Keywords
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- EP ID EP759520
- DOI https://doi.org/10.56578/ida030403
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