One-step learning algorithm selection for classification via convolutional neural networks

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dc.coverageDOI: 10.1016/j.ins.2025.122610
dc.creatorMaldonado, Sebastián
dc.creatorVairetti, Carla
dc.creatorFigueroa, Ignacio
dc.date2025
dc.date.accessioned2026-01-05T21:17:07Z
dc.date.available2026-01-05T21:17:07Z
dc.description<p>As with any task, the process of building machine learning models can benefit from prior experience. Meta-learning for classifier selection leverages knowledge about the characteristics of different datasets and/or the past performance of machine learning techniques to inform better decisions in the current modeling process. Traditional meta-learning approaches first collect metadata that describe this prior experience and then use it as input for an algorithm selection model. In this paper, however, a one-step scheme is proposed in which convolutional neural networks are trained directly on tabular datasets for binary classification. The aim is to learn the underlying structure of the data without the need to explicitly identify meta-features. Experiments with simulated datasets show that the proposed approach achieves near-perfect performance in identifying both linear and nonlinear patterns, outperforming the conventional two-step method based on meta-features. The method is further applied to real-world datasets, providing recommendations on the most suitable classifiers based on the data's inherent structure.</p>eng
dc.identifierhttps://investigadores.uandes.cl/en/publications/4a5279fe-2b78-4c12-bfe0-119a325650b1
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/67408
dc.languageeng
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.721 (2025)
dc.subjectAlgorithm selection
dc.subjectClassifier selection
dc.subjectMachine learning
dc.subjectMeta-learning
dc.titleOne-step learning algorithm selection for classification via convolutional neural networkseng
dc.typeArticleeng
dc.typeArtículospa
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