Decision tree learning for uncertain clinical measurements

  • Authors
  • Nunes C, Langet H, De Craene M, Camara O, Bijnens B, Jonsson A
  • UPF authors
  • JONSSON ., PER ANDERS; NUNES, CECILIA MARIA COSTA BENTO SOUSA; CAMARA REY, OSCAR; BIJNENS, BART H;
  • Type
  • Scholarly articles
  • Journal títle
  • IEEE Transactions on Knowledge and Data Engineering
  • Publication year
  • 2020
  • Number
  • 16 january
  • Pages
  • 0-0
  • ISSN
  • 1041-4347
  • Publication State
  • Published
  • Abstract
  • approach in the distinct phases of DT learning, nor when the uncertainty is present in the training or the test data. We present a probabilistic DT approach that models measurement uncertainty as a noise distribution, independently realized: (1) when searching for the split thresholds, (2) when splitting the training instances, and (3) when generating predictions for unseen data. The soft training approaches (1, 2) achieved a regularizing effect, leading to significant reductions in DT size, while maintaining accuracy, for increased noise. Soft evaluation (3) showed no benefit in handling noise
  • Complete citation
  • Nunes C, Langet H, De Craene M, Camara O, Bijnens B, Jonsson A. Decision tree learning for uncertain clinical measurements. IEEE Transactions on Knowledge and Data Engineering 2020; (16 january).
Bibliometric indicators
  • 0 times cited Scopus
  • 0 times cited WOS
  • Índex Scimago de 1.36(2020)