References in zbMATH (referenced in 1127 articles )

Showing results 1 to 20 of 1127.
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  1. Bénard, Clément; Biau, Gérard; Da Veiga, Sébastien; Scornet, Erwan: SIRUS: stable and interpretable RUle set for classification (2021)
  2. Bruto da Costa, Antonio Anastasio; Dasgupta, Pallab: Learning temporal causal sequence relationships from real-time time-series (2021)
  3. Burkart, Nadia; Huber, Marco F.: A survey on the explainability of supervised machine learning (2021)
  4. Gan, Guojun; Ma, Chaoqun; Wu, Jianhong: Data clustering. Theory, algorithms, and applications (2021)
  5. Beaulac, Cédric; Rosenthal, Jeffrey S.: BEST: a decision tree algorithm that handles missing values (2020)
  6. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  7. Chelly Dagdia, Zaineb; Elouedi, Zied: A hybrid fuzzy maintained classification method based on dendritic cells (2020)
  8. De Bock, Koen W.; Coussement, Kristof; Lessmann, Stefan: Cost-sensitive business failure prediction when misclassification costs are uncertain: a heterogeneous ensemble selection approach (2020)
  9. Fujita, Hamido; Ko, Yu-Chien: A heuristic representation learning based on evidential memberships: case study of UCI-SPECTF (2020)
  10. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  11. Jacobs, Kayla; Itai, Alon; Wintner, Shuly: Acronyms: identification, expansion and disambiguation (2020)
  12. 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)
  13. Nguyen, Thi Thanh Sang; Do, Pham Minh Thu: Classification optimization for training a large dataset with naïve Bayes (2020)
  14. 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)
  15. Ruehle, Fabian: Data science applications to string theory (2020)
  16. Shakerin, Farhad; Gupta, Gopal: White-box induction from SVM models: explainable AI with logic programming (2020)
  17. Stańczyk, U.; Zielosko, B.: Heuristic-based feature selection for rough set approach (2020)
  18. Wang, Jie; Wang, Bo; Liang, Jing; Yu, Kunjie; Yue, Caitong; Ren, Xiangyang: Ensemble learning via multimodal multiobjective differential evolution and feature selection (2020)
  19. Yang, Shiueng-Bien; Chen, Tai-Liang: Uncertain decision tree for bank marketing classification (2020)
  20. Armengol, Eva; Boixader, Dionís; García-Cerdaña, Àngel; Recasens, Jordi: (T)-generable indistinguishability operators and their use for feature selection and classification (2019)

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