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C443: a methodology to see a forest for the trees

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Publication:2220704
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DOI10.1007/s00357-019-09350-4OpenAlexW2998871975WikidataQ126398129 ScholiaQ126398129MaRDI QIDQ2220704

Aniek Sies, Iven van Mechelen

Publication date: 25 January 2021

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://lirias.kuleuven.be/handle/123456789/643567

zbMATH Keywords

clusteringclassification treesbaggingensemble methodsstatistical learning


Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)


Related Items

SUBiNN: a stacked uni- and bivariate \(k\)NN sparse ensemble


Uses Software

  • Silhouettes
  • UCI-ml
  • AdaBoost.MH
  • ElemStatLearn
  • clusfind


Cites Work

  • Unnamed Item
  • Unnamed Item
  • Bagging predictors
  • The comparison between classification trees through proximity measures
  • A similarity measure to assess the stability of classification trees
  • Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
  • Heuristics of instability and stabilization in model selection
  • A decision-theoretic generalization of on-line learning and an application to boosting
  • Bagging, boosting and the random subspace method for linear classifiers
  • A Method for Comparing Two Hierarchical Clusterings
  • Finding Groups in Data
  • Random forests
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