Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The paper presents an ensemble classification method based on clustering, along with its implementation in the Python programming language. An illustrative example showing the method behavior is provided, and the results of a computational experiment performed on real life data sets are reported.
Słowa kluczowe
Rocznik
Tom
Strony
7--22
Opis fizyczny
Bibliogr. 10 poz., rys., tab., wykr.
Twórcy
autor
- Warsaw School of Computer Science
autor
- Warsaw School of Computer Science
Bibliografia
- [1] D. Faggella, What is Machine Learning, https://emerj.com/ai-glossaryterms/what-is-machine-learning/ [20 August 2019].
- [2] D.T. Larose, Discovering knowledge in data, New Jersey: John Wiley & Sons, Inc., 2005.
- [3] H. Xiao, Z. Xiao, Y. Wang, Ensemble classification based on supervised clustering for credit scoring, Applied Soft Computing 43, p. 73-86, 2016.
- [4] R. Gandhi, Support Vector Machine - Introduction to Machine Learning Algorithms, https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47 [20 August 2019].
- [5] S.I. Serengil, Chefboost, https://github.com/serengil/chefboost [20 August 2019].
- [6] Anaconda, https://www.anaconda.com/ [20 August 2019].
- [7] Scikit-learn, https://scikit-learn.org/ [20 August 2019].
- [8] V. Lohweg, Banknote Authentication Data Set, University of Applied Sciences, Ostwestfalen-Lippe, http://archive.ics.uci.edu/ml/datasets/banknote+authentication [20 August 2019].
- [9] H. Hofmann, German Credit Data Set, University of Hamburg, https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data) [20 August 2019].
- [10] T.-S. Lim, Haberman’s Survival Data Set, University of Chicago’s Billings Hospital, http://archive.ics.uci.edu/ml/datasets/Haberman%27s+Survival [20 August 2019].
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5accd9b0-e1fc-4383-928f-8399170f88ca