Machine Learning Classification of Peaceful Countries: A Comparative Analysis and Dataset Optimization
<|reference_start|>Machine Learning Classification of Peaceful Countries: A Comparative Analysis and Dataset Optimization: This paper presents a machine learning approach to classify countries as peaceful or non-peaceful using linguistic patterns extracted from global media articles. We employ vector embeddings and cosine similarity to develop a supervised classification model that effectively identifies peaceful countries. Additionally, we explore the impact of dataset size on model performance, investigating how shrinking the dataset influences classification accuracy. Our results highlight the challenges and opportunities associated with using large-scale text data for peace studies.<|reference_end|>
@article{lian2024machine,
    title={Machine Learning Classification of Peaceful Countries: A Comparative
  Analysis and Dataset Optimization},
    author={K. Lian (1), L. S. Liebovitch (1), M. Wild (1), H. West (1), P. T.
  Coleman (1), F. Chen (2), E. Kimani (2), K. Sieck (2) ((1) Columbia
  University, (2) Toyota Research Institute)},
    journal={arXiv preprint arXiv:2410.03749},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03749},
    primaryClass={cs.CL cs.LG}
}