Random forest kernel for high-dimension low sample size classification - Archive ouverte HAL
Article Dans Une Revue Statistics and Computing Année : 2024

Random forest kernel for high-dimension low sample size classification

Lucca Portes Cavalheiro
  • Fonction : Auteur
Simon Bernard
Jean Paul Barddal
  • Fonction : Auteur
Laurent Heutte
  • Fonction : Auteur

Résumé

High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing, traditional machine learning algorithms are usually unsuccessful in learning the best possible concept from such data. In a previous work, we proposed a dissimilarity-based approach for multi-view classification, the Random Forest Dissimilarity (RFD), that perfoms state-of-the-art results for such problems. In this work, we transpose the core principle of this approach to solving HDLSS classification problems, by using the RF similarity measure as a learned precomputed SVM kernel (RFSVM). We show that such a learned similarity measure is particularly suited and accurate for this classification context. Experiments conducted on 40 public HDLSS classification datasets, supported by rigorous statistical analyses, show that the RFSVM method outperforms existing methods for the majority of HDLSS problems and remains at the same time very competitive for low or non-HDLSS problems.
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Dates et versions

hal-04253909 , version 1 (23-10-2023)

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Lucca Portes Cavalheiro, Simon Bernard, Jean Paul Barddal, Laurent Heutte. Random forest kernel for high-dimension low sample size classification. Statistics and Computing, 2024, 34 (1), pp.9. ⟨10.1007/s11222-023-10309-0⟩. ⟨hal-04253909⟩
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