Tenfold Bootstrap as Resampling Method in Classification Problems

Borislava Vrigazova

A chapter in Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020, 2020, pp 74-83 from IRENET - Society for Advancing Innovation and Research in Economy, Zagreb

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.

Keywords: the bootstrap; cross validation; repeated train/test splitting (search for similar items in EconPapers)
JEL-codes: C38 C52 C55 (search for similar items in EconPapers)
Date: 2020
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