IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i16p2867-d885735.html
   My bibliography  Save this article

Improving Large-Scale k -Nearest Neighbor Text Categorization with Label Autoencoders

Author

Listed:
  • Francisco J. Ribadas-Pena

    (Department of Computer Science, University of Vigo, Edificio Politécnico, Campus As Lagoas s/n, 32004 Ourense, Spain
    These authors contributed equally to this work.)

  • Shuyuan Cao

    (Department of Computer Science, University of Vigo, Edificio Politécnico, Campus As Lagoas s/n, 32004 Ourense, Spain
    These authors contributed equally to this work.)

  • Víctor M. Darriba Bilbao

    (Department of Computer Science, University of Vigo, Edificio Politécnico, Campus As Lagoas s/n, 32004 Ourense, Spain
    These authors contributed equally to this work.)

Abstract

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.

Suggested Citation

  • Francisco J. Ribadas-Pena & Shuyuan Cao & Víctor M. Darriba Bilbao, 2022. "Improving Large-Scale k -Nearest Neighbor Text Categorization with Label Autoencoders," Mathematics, MDPI, vol. 10(16), pages 1-22, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2867-:d:885735
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/16/2867/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/16/2867/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Grigorios Tsoumakas & Ioannis Katakis, 2007. "Multi-Label Classification: An Overview," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 1-13, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Radu Cristian Alexandru Iacob & Vlad Cristian Monea & Dan Rădulescu & Andrei-Florin Ceapă & Traian Rebedea & Ștefan Trăușan-Matu, 2020. "AlgoLabel: A Large Dataset for Multi-Label Classification of Algorithmic Challenges," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    2. Azzini, Antonia & Cortesi, Nicola & Marrara, Stefania & Topalović, Amir, 2019. "A Multi-Label Machine Learning Approach to Support Pathologist's Histological Analysis," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, pages 197-208, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    3. Xueying Zhang & Qinbao Song, 2015. "A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-30, April.
    4. Junming Yin & Jerry Luo & Susan A. Brown, 2021. "Learning from Crowdsourced Multi-labeling: A Variational Bayesian Approach," Information Systems Research, INFORMS, vol. 32(3), pages 752-773, September.
    5. Bocheng Li & Yunqiu Zhang & Xusheng Wu, 2022. "DLKN-MLC: A Disease Prediction Model via Multi-Label Learning," IJERPH, MDPI, vol. 19(15), pages 1-15, August.
    6. Hamid Bekamiri & Daniel S. Hain & Roman Jurowetzki, 2021. "PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT," Papers 2103.11933, arXiv.org, revised Oct 2021.
    7. Chaker Jebari, 2016. "Multi-Label Genre Classification of Web Pages Using an Adaptive Centroid-Based Classifier," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 1-21, March.
    8. Tao Shu & Zhiyi Wang & Huading Jia & Wenjin Zhao & Jixian Zhou & Tao Peng, 2022. "Consumers’ Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China," IJERPH, MDPI, vol. 19(19), pages 1-19, October.
    9. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.
    10. Yi-Hui Chen & Eric Jui-Lin Lu & Yu-Ting Lin & Ya-Wen Cheng, 2016. "Document overlapping clustering using formal concept analysis," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 2(2), pages 28-34.
    11. D. Thorleuchter & D. Van Den Poel, 2013. "Semantic Compared Cross Impact Analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/862, Ghent University, Faculty of Economics and Business Administration.
    12. Han Zou & Jing Ge & Ruichao Liu & Lin He, 2023. "Feature Recognition of Regional Architecture Forms Based on Machine Learning: A Case Study of Architecture Heritage in Hubei Province, China," Sustainability, MDPI, vol. 15(4), pages 1-27, February.
    13. Josef Schwaiger & Timo Hammerl & Johannsen Florian & Susanne Leist, 2021. "UR: SMART–A tool for analyzing social media content," Information Systems and e-Business Management, Springer, vol. 19(4), pages 1275-1320, December.
    14. Verwaeren, Jan & Waegeman, Willem & De Baets, Bernard, 2012. "Learning partial ordinal class memberships with kernel-based proportional odds models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 928-942.
    15. Huazhen Wang & Xin Liu & Bing Lv & Fan Yang & Yanzhu Hong, 2014. "Reliable Multi-Label Learning via Conformal Predictor and Random Forest for Syndrome Differentiation of Chronic Fatigue in Traditional Chinese Medicine," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-14, June.
    16. D. Thorleuchter & D. Van Den Poel & A. Prinzie & -, 2010. "A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/632, Ghent University, Faculty of Economics and Business Administration.
    17. Debaere, Steven & Coussement, Kristof & De Ruyck, Tom, 2018. "Multi-label classification of member participation in online innovation communities," European Journal of Operational Research, Elsevier, vol. 270(2), pages 761-774.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2867-:d:885735. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.