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Tenfold Bootstrap as Resampling Method in Classification Problems

In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020

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  • Vrigazova, Borislava

Abstract

In this research, we propose the bootstrap procedure as a method for train/test splitting in machine learning algorithms for classification. We show that this resampling method can be a reliable alternative to cross validation and repeated random test/train splitting algorithms. The bootstrap procedure optimizes the classifier's performance by improving its accuracy and classification scores and by reducing computational time significantly. We also show that ten iterations of the bootstrap procedure are enough to achieve better performance of the classification algorithm. With these findings, we propose a solution to the problem of how to reduce computing time in large datasets, while introducing a new practical application of the bootstrap procedure.

Suggested Citation

  • Vrigazova, Borislava, 2020. "Tenfold Bootstrap as Resampling Method in Classification Problems," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2020), Virtual Conference, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020, pages 74-83, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    • Handle: RePEc:zbw:entr20:224677
    as

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    References listed on IDEAS

    as
    1. James G. MacKinnon, 2002. "Bootstrap inference in econometrics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 35(4), pages 615-645, November.
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    More about this item

    Keywords

    the bootstrap; cross validation; repeated train/test splitting;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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