References in zbMATH (referenced in 14 articles )

Showing results 1 to 14 of 14.
Sorted by year (citations)

  1. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  2. Szymon Maksymiuk, Alicja Gosiewska, Przemyslaw Biecek: Landscape of R packages for eXplainable Artificial Intelligence (2020) arXiv
  3. Jin, Ick Hoon; Jeon, Minjeong: A doubly latent space joint model for local item and person dependence in the analysis of item response data (2019)
  4. M. Cristina Heredia-Gómez; Salvador García; Pedro Antonio Gutiérrez; Francisco Herrera: OCAPIS: R package for Ordinal Classification And Preprocessing In Scala (2018) arXiv
  5. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  6. Janitza, Silke; Tutz, Gerhard; Boulesteix, Anne-Laure: Random forest for ordinal responses: prediction and variable selection (2016)
  7. Tutz, Gerhard; Koch, Dominik: Improved nearest neighbor classifiers by weighting and selection of predictors (2016)
  8. Kotthaus, Helena; Korb, Ingo; Lang, Michel; Bischl, Bernd; Rahnenführer, Jörg; Marwedel, Peter: Runtime and memory consumption analyses for machine learning R programs (2015)
  9. Kubrusly, Jessica; Lopes, Hélio: Constructive regression on implicit regions (2015)
  10. Barranquero, Jose; González, Pablo; Díez, Jorge; Del Coz, Juan José: On the study of nearest neighbor algorithms for prevalence estimation in binary problems (2013)
  11. Bischl, Bernd; Schiffner, Julia; Weihs, Claus: Benchmarking local classification methods (2013)
  12. Robinson, Andrew P.; Hamann, Jeff D.: Forest analytics with R (2011)
  13. Kang, Pilsung; Cho, Sungzoon: Locally linear reconstruction for instance-based learning (2008)
  14. Zong-chang, Yang: A vector gravitational force model for classification (2007) ioport