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
Related Articles
- EP ID EP759520
- DOI https://doi.org/10.56578/ida030403
- Views 47
- Downloads 1
Critical Factors Influencing Cloud Security Posture of Enterprises: An Empirical Analysis
This study examines the aspects that can impact an organization's cloud security posture and the consequences for their cloud adoption strategies. Based on a thorough examination of existing literature, a conceptual fram...
Enhanced Defect Detection in Insulator Iron Caps Using Improved YOLOv8n
To address the challenges in detecting surface defects on insulator iron caps, particularly due to the complex backgrounds that hinder accurate identification, an improved defect detection algorithm based on YOLOv8n, w...
An Integrated BERT-XGBoost Framework for Open-Source Intelligence Classification in Aerospace Technology
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 classifica...
Extraction of Judgment Elements from Legal Instruments Using an Attention Mechanism-Based RCNN Fusion Model
In the field of jurisprudence, judgment element extraction has become a crucial aspect of legal judgment prediction research. The introduction of pre-trained language models has provided significant momentum for the adva...
Advancements in Cow Health Monitoring: A Systematic Literature Review of IoT Applications
The landscape of livestock farming is undergoing a significant transformation, primarily influenced by the integration of the Internet of Things (IoT) technology. This systematic literature review (SLR) critically examin...