Is K-fold cross validation the best model selection method for machine learning?
J. M. Górriz; R. Martin-Clemente; F. Segovia; J. Ramírez; Andrés Ortíz; John Suckling · 2026 · Information Fusion
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference in multi-source and heterogeneous information fusion scenarios. K-fold cross-validation (CV) is the most common approach for ascertaining the likelihood that a machine learning outcome is generated by chance and frequently outperforms conventional hypothesis testing. This improvement arises from measures directly obtained from machine learning classifications, such as accuracy, that do not have a parametric description. To approach a frequentist analysis within fusi
Abstract by J. M. Górriz; R. Martin-Clemente; F. Segovia; J. Ramírez; Andrés Ortíz; John Suckling, Information Fusion (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1016/j.inffus.2026.104404