References in zbMATH (referenced in 1086 articles )

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  1. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  2. Mohanty, Monalisa; Biswal, Pradyut; Sabut, Sukanta: Machine learning approach to recognize ventricular arrhythmias using VMD based features (2020)
  3. Yang, Shiueng-Bien; Chen, Tai-Liang: Uncertain decision tree for bank marketing classification (2020)
  4. Armengol, Eva; Boixader, Dionís; García-Cerdaña, Àngel; Recasens, Jordi: (T)-generable indistinguishability operators and their use for feature selection and classification (2019)
  5. Boullé, Marc; Charnay, Clément; Lachiche, Nicolas: A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data (2019)
  6. Bruni, Renato; Bianchi, Gianpiero; Dolente, Cosimo; Leporelli, Claudio: Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior (2019)
  7. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  8. Dvořák, Jakub: Classification trees with soft splits optimized for ranking (2019)
  9. Karaca, Yeliz; Cattani, Carlo: Computational methods for data analysis (2019)
  10. Livieris, Ioannis E.; Kanavos, Andreas; Tampakas, Vassilis; Pintelas, Panagiotis: A weighted voting ensemble self-labeled algorithm for the detection of lung abnormalities from X-rays (2019)
  11. Poterie, A.; Dupuy, J.-F.; Monbet, V.; Rouvière, L.: Classification tree algorithm for grouped variables (2019)
  12. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  13. Ramosaj, Burim; Pauly, Markus: Predicting missing values: a comparative study on non-parametric approaches for imputation (2019)
  14. Razzaghi, Talayeh; Safro, Ilya; Ewing, Joseph; Sadrfaridpour, Ehsan; Scott, John D.: Predictive models for bariatric surgery risks with imbalanced medical datasets (2019)
  15. Trabelsi, Asma; Elouedi, Zied; Lefevre, Eric: Decision tree classifiers for evidential attribute values and class labels (2019)
  16. Tsou, Yu-Lin; Lin, Hsuan-Tien: Annotation cost-sensitive active learning by tree sampling (2019)
  17. Wang, Lidong; Zhang, Ruijun; Mu, Yashuang: Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou’s general PseAAC (2019)
  18. Wang, Wenxi; Søndergaard, Harald; Stuckey, Peter J.: Wombit: a portfolio bit-vector solver using word-level propagation (2019)
  19. Aggarwal, Charu C.: Machine learning for text (2018)
  20. Alsolami, Fawaz; Amin, Talha; Chikalov, Igor; Moshkov, Mikhail: Bi-criteria optimization problems for decision rules (2018)

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