An Integrated BERT-XGBoost Framework for Open-Source Intelligence Classification in Aerospace Technology
Year: 2024
Type: article
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.
DOI: 10.56578/ida030403
Authors: Suping Yu, Weiwei Mao
Institution: Luoyang Institute of Science and Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407709313