首页|Enhancing associative classification on imbalanced data through ontology-based feature extraction and resampling

Enhancing associative classification on imbalanced data through ontology-based feature extraction and resampling

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  • NETL
  • NSTL
  • Elsevier
© 2024Associative classification models are valuable for discovering relationships within heterogeneous data systems, making them particularly useful for data integration tasks. However, they struggle with imbalanced and sparse data. This paper addresses the problem of imbalanced classification in building maintenance data by providing several updates based both on algorithms and preprocessing. Experiments conducted on real maintenance datasets demonstrate significant improvements in accuracy and precision.

Building maintenanceFeature extractionImbalanced associative classificationOntologiesOversampling

Mba Kouhoue J.、Lonlac J.、Doniec A.、Lesage A.、Lecoeuche S.

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IMT Nord Europe Institut Mines-Télécom University of Lille.

Intent Technologies

IMT Mines Alès

2025

10.1016/j.knosys.2024.112897

Knowledge-based systems

Knowledge-based systems

SCI
ISSN：0950-7051
年,卷(期)：2025.309(Jan.30)
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