Hyperparameter optimization of two-branch neural networks in multi-target prediction

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • Dimitrios Iliadis
  • Marcel Wever
  • Bernard De baets
  • Willem Waegeman

Externe Organisationen

  • Universiteit Gent
  • Ludwig-Maximilians-Universität München (LMU)
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Aufsatznummer111957
Seiten (von - bis)111957
FachzeitschriftApplied soft computing
Jahrgang165
Frühes Online-Datum8 Juli 2024
PublikationsstatusVeröffentlicht - Nov. 2024
Extern publiziertJa

Abstract

As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and Auto-sklearn typically focus on classical machine learning (ML) tasks such as classification and regression. Our work can be seen as the first attempt at offering a single AutoML framework for most problem settings that fall under the umbrella of multi-target prediction, which includes popular ML settings such as multi-label classification, multivariate regression, multi-task learning, dyadic prediction, matrix completion, and zero-shot learning. Automated problem selection and model configuration are achieved by extending DeepMTP, a general deep learning framework for MTP problem settings, with popular hyperparameter optimization (HPO) methods. Our extensive benchmarking across different datasets and MTP problem settings identifies cases where specific HPO methods outperform others.

ASJC Scopus Sachgebiete

Zitieren

Hyperparameter optimization of two-branch neural networks in multi-target prediction. / Iliadis, Dimitrios; Wever, Marcel; De baets, Bernard et al.
in: Applied soft computing, Jahrgang 165, 111957, 11.2024, S. 111957.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Iliadis D, Wever M, De baets B, Waegeman W. Hyperparameter optimization of two-branch neural networks in multi-target prediction. Applied soft computing. 2024 Nov;165:111957. 111957. Epub 2024 Jul 8. doi: 10.1016/j.asoc.2024.111957
Iliadis, Dimitrios ; Wever, Marcel ; De baets, Bernard et al. / Hyperparameter optimization of two-branch neural networks in multi-target prediction. in: Applied soft computing. 2024 ; Jahrgang 165. S. 111957.
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