Publisher
Springer Nature Switzerland
Reference49 articles.
1. Rathore, S.S., Kumar, S.: Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems. Knowl. Based Syst. 119, 232–256 (2017)
2. Kotsiantis, S., Zaharakis, I., Pintelas, P.: Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26(3), 159–190 (2006)
3. Chamikara, M., Bertok, P., Khalil, I., Liu, D., Camtepe, S.: Privacy preserving distributed machine learning with federated learning. Comput. Commun. 171, 112–125 (2021)
4. Froelicher, D., et al.: Scalable privacy-preserving distributed learning. In: Proceedings on Privacy Enhancing Technologies, pp.323–347 (2021)
5. Ulianova, S. “Cardiovascular Disease dataset”, kaggle (2019). https://www.kaggle.com/sulianova/cardiovascular-disease-dataset. Accessed 18 Dec 2021