References in zbMATH (referenced in 1120 articles )

Showing results 1 to 20 of 1120.
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  1. Beaulac, Cédric; Rosenthal, Jeffrey S.: BEST: a decision tree algorithm that handles missing values (2020)
  2. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  3. Chelly Dagdia, Zaineb; Elouedi, Zied: A hybrid fuzzy maintained classification method based on dendritic cells (2020)
  4. De Bock, Koen W.; Coussement, Kristof; Lessmann, Stefan: Cost-sensitive business failure prediction when misclassification costs are uncertain: a heterogeneous ensemble selection approach (2020)
  5. Fujita, Hamido; Ko, Yu-Chien: A heuristic representation learning based on evidential memberships: case study of UCI-SPECTF (2020)
  6. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  7. Jacobs, Kayla; Itai, Alon; Wintner, Shuly: Acronyms: identification, expansion and disambiguation (2020)
  8. Khan, Zardad; Gul, Asma; Perperoglou, Aris; Miftahuddin, Miftahuddin; Mahmoud, Osama; Adler, Werner; Lausen, Berthold: Ensemble of optimal trees, random forest and random projection ensemble classification (2020)
  9. Mohanty, Monalisa; Biswal, Pradyut; Sabut, Sukanta: Machine learning approach to recognize ventricular arrhythmias using VMD based features (2020)
  10. Nguyen, Thi Thanh Sang; Do, Pham Minh Thu: Classification optimization for training a large dataset with naïve Bayes (2020)
  11. Rivolli, Adriano; Read, Jesse; Soares, Carlos; Pfahringer, Bernhard; de Carvalho, André C. P. L. F.: An empirical analysis of binary transformation strategies and base algorithms for multi-label learning (2020)
  12. Ruehle, Fabian: Data science applications to string theory (2020)
  13. Stańczyk, U.; Zielosko, B.: Heuristic-based feature selection for rough set approach (2020)
  14. Wang, Jie; Wang, Bo; Liang, Jing; Yu, Kunjie; Yue, Caitong; Ren, Xiangyang: Ensemble learning via multimodal multiobjective differential evolution and feature selection (2020)
  15. Yang, Shiueng-Bien; Chen, Tai-Liang: Uncertain decision tree for bank marketing classification (2020)
  16. Armengol, Eva; Boixader, Dionís; García-Cerdaña, Àngel; Recasens, Jordi: (T)-generable indistinguishability operators and their use for feature selection and classification (2019)
  17. Boullé, Marc; Charnay, Clément; Lachiche, Nicolas: A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data (2019)
  18. Bruni, Renato; Bianchi, Gianpiero; Dolente, Cosimo; Leporelli, Claudio: Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior (2019)
  19. 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)
  20. Ditzler, Gregory; Miller, Sean; Rozenblit, Jerzy: Learning what we don’t care about: anti-training with sacrificial functions (2019)

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