References in zbMATH (referenced in 52 articles )

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  1. Höppner, Sebastiaan; Baesens, Bart; Verbeke, Wouter; Verdonck, Tim: Instance-dependent cost-sensitive learning for detecting transfer fraud (2022)
  2. Silva, Diego M. B.; Pereira, Gustavo H. A.; Magalhães, Tiago M.: A class of categorization methods for credit scoring models (2022)
  3. Civitelli, Enrico; Lapucci, Matteo; Schoen, Fabio; Sortino, Alessio: An effective procedure for feature subset selection in logistic regression based on information criteria (2021)
  4. Gunnarsson, Björn Rafn; vanden Broucke, Seppe; Baesens, Bart; Óskarsdóttir, María; Lemahieu, Wilfried: Deep learning for credit scoring: do or don’t? (2021)
  5. Krasanakis, Emmanouil; Symeonidis, Andreas: Defining behaviorizeable relations to enable inference in semi-automatic program synthesis (2021)
  6. Yu, Peiran; Pong, Ting Kei; Lu, Zhaosong: Convergence rate analysis of a sequential convex programming method with line search for a class of constrained difference-of-convex optimization problems (2021)
  7. Yu, Tengchao; Wang, Hongqiao; Li, Jinglai: Maximum conditional entropy Hamiltonian Monte Carlo sampler (2021)
  8. Zhang, Xu; Tian, Yahui; Guan, Guoyu; Gel, Yulia R.: Depth-based classification for relational data with multiple attributes (2021)
  9. Brentnall, Adam R.; Cuzick, Jack: Risk models for breast cancer and their validation (2020)
  10. McManus, Scott; Rahman, Azizur; Horta, Ana; Coombes, Jacqueline: Applied Bayesian modeling for assessment of interpretation uncertainty in spatial domains (2020)
  11. Naka, Poontavika; Boado-Penas, María del Carmen; Lanot, Gauthier: A multiple state model for the working-age disabled population using cross-sectional data (2020)
  12. Orozco-Acosta, Erick; Llinás-Solano, Humberto; Fonseca-Rodríguez, Javier: Convergence theorems in multinomial saturated and logistic models (2020)
  13. Srinivasan, Shriram; Cawi, Eric; Hyman, Jeffrey; Osthus, Dave; Hagberg, Aric; Viswanathan, Hari; Srinivasan, Gowri: Physics-informed machine learning for backbone identification in discrete fracture networks (2020)
  14. Wang, Ximei; Hu, Min; Zhao, Yanlong; Djehiche, Boualem: Credit scoring based on the set-valued identification method (2020)
  15. Bersimis, Fragkiskos G.; Panagiotakos, Demosthenes; Vamvakari, Malvina: The use of components’ weights improves the diagnostic accuracy of a health-related index (2019)
  16. Carpita, Maurizio; Ciavolino, Enrico; Pasca, Paola: Exploring and modelling team performances of the kaggle European soccer database (2019)
  17. Coskun, Burcin; Alpu, O.: Diagnostics of multiple group influential observations for logistic regression models (2019)
  18. Minary, Pauline; Pichon, Frédéric; Mercier, David; Lefevre, Eric; Droit, Benjamin: Evidential joint calibration of binary SVM classifiers (2019)
  19. Roy, Asim; Qureshi, Shiban; Pande, Kartikeya; Nair, Divitha; Gairola, Kartik; Jain, Pooja; Singh, Suraj; Sharma, Kirti; Jagadale, Akshay; Lin, Yi-Yang; Sharma, Shashank; Gotety, Ramya; Zhang, Yuexin; Tang, Ji; Mehta, Tejas; Sindhanuru, Hemanth; Okafor, Nonso; Das, Santak; Gopal, Chidambara N.; Rudraraju, Srinivasa B.; Kakarlapudi, Avinash V.: Performance comparison of machine learning platforms (2019)
  20. Schaeben, H.; Kost, S.; Semmler, G.: Popular raster-based methods of prospectivity modeling and their relationships (2019)

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