Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders
<|reference_start|>Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders: In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label correlation. The proposed method is an evolution of the traditional k-Nearest Neighbors algorithm which uses a large autoencoder trained to map the large label space to a reduced size latent space and to regenerate the predicted labels from this latent space. We have evaluated our proposal in a large portion of the MEDLINE biomedical document collection which uses the Medical Subject Headings (MeSH) thesaurus as a controlled vocabulary. In our experiments we propose and evaluate several document representation approaches and different label autoencoder configurations.<|reference_end|>
@article{ribadas-pena2024improving,
    title={Improving Large-Scale k-Nearest Neighbor Text Categorization with Label
  Autoencoders},
    author={Francisco J. Ribadas-Pena, Shuyuan Cao, V'ictor M. Darriba Bilbao},
    journal={Mathematics 2022, 10(16), 2867},
    year={2024},
    doi={10.3390/math10162867},
    archivePrefix={arXiv},
    eprint={2402.01963},
    primaryClass={cs.LG cs.CL cs.IR}
}