Multi-stage transfer learning with an application to selection process

Andre Mendes, Julian Togelius, Leandro Dos Santos Coelho

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In multi-stage processes, decisions happen in an ordered sequence of stages. Many of them have the structure of dual funnel problem: As the sample size decreases from one stage to the other, the information increases. A related example is a selection process, where applicants apply for a position, prize or grant. In each stage, more applicants are evaluated and filtered out and from the remaining ones, more information is collected. In the last stage, decision-makers use all available information to make their final decision. To train a classifier for each stage becomes impracticable as they can underfit due to the low dimensionality in early stages or overfit due to the small sample size in the latter stages. In this work, we proposed a Multi-StaGe Transfer Learning (MSGTL) approach that uses knowledge from simple classifiers trained in early stages to improve the performance of classifiers in the latter stages. By transferring weights from simpler neural networks trained in larger datasets, we able to fine-tune more complex neural networks in the latter stages without overfitting due to the small sample size. We show that is possible to control the trade-off between conserving knowledge and fine-tuning using a simple probabilistic map. Experiments using real-world data show the efficacy of our approach as it outperforms other state-of-the-art methods for transfer learning and regularization.

    Original languageEnglish (US)
    Title of host publicationECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings
    EditorsGiuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senen Barro, Alberto Bugarin, Jerome Lang
    PublisherIOS Press BV
    Pages1770-1777
    Number of pages8
    ISBN (Electronic)9781643681009
    DOIs
    StatePublished - Aug 24 2020
    Event24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Santiago de Compostela, Online, Spain
    Duration: Aug 29 2020Sep 8 2020

    Publication series

    NameFrontiers in Artificial Intelligence and Applications
    Volume325
    ISSN (Print)0922-6389

    Conference

    Conference24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020
    CountrySpain
    CitySantiago de Compostela, Online
    Period8/29/209/8/20

    ASJC Scopus subject areas

    • Artificial Intelligence

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