1. bookTom 9 (2018): Zeszyt 2 (July 2018)
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
1847-9375
Pierwsze wydanie
19 Sep 2012
Częstotliwość wydawania
2 razy w roku
Języki
Angielski
Open Access

Number of Instances for Reliable Feature Ranking in a Given Problem

Data publikacji: 28 Jul 2018
Tom & Zeszyt: Tom 9 (2018) - Zeszyt 2 (July 2018)
Zakres stron: 35 - 44
Otrzymano: 31 Jan 2018
Przyjęty: 21 Apr 2018
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
1847-9375
Pierwsze wydanie
19 Sep 2012
Częstotliwość wydawania
2 razy w roku
Języki
Angielski

1. Beleites, C., Neugebauer U., Bocklitz T., Krafft, C., Popp, J. (2013), “Sample size planning for classification models”, Analytica Chimica Acta, Vol. 760, pp. 25-33.10.1016/j.aca.2012.11.007Search in Google Scholar

2. Bohanec, M. (2017), “A public B2B data set used for qualitative sales forecasting research”, available at: http://www.salvirt.com/research/B2Bdataset/ (01 August 2017).Search in Google Scholar

3. Bohanec, M., Kljajić Borštnar, M., Robnik-Šikonja, M. (2015a), “Feature subset selection for B2B sales forecasting”, in Zadnik Stirn L., Žerovnik J., Kljajić Borštnar M., Drobne S. (Eds.), 13th International Symposium on Operational Research, SDI-SOR, Bled, Slovenia, pp. 285-290.Search in Google Scholar

4. Bohanec, M., Kljajić Borštnar, M., Robnik-Šikonja, M. (2015b), “Machine learning data set analysis with visual simulation”, in Kljajić L., Lasker G. E. (Eds.), Advances in simulationbased decision support & business intelligence, Vol. 5, Tecumseh: International Institute for Advanced Studies in Systems Research and Cybernetics, Baden-Baden, Germany, pp. 16-20.Search in Google Scholar

5. Bohanec, M., Kljajić Borštnar, M., Robnik-Šikonja, M. (2016), “Sample size for identification of important attributes in B2B sales”, in Scitovski R., Zekić-Sušac M. (Eds.), 16th International Conference on Operational Research, CRORS, Osijek, Croatia, p. 133.Search in Google Scholar

6. Davison, A. C., Hinkley, D. V. (1997), Bootstrap methods and their application, Vol. 1, Cambridge University Press.10.1017/CBO9780511802843Search in Google Scholar

7. Figueroa, R. L., Zeng-Treitler, Q., Kandula, S., Ngo, L. H. (2012), “Predicting sample size required for classification performance”, BMC medical informatics and decision making, Vol. 12, No. 1, pp. 1-8.10.1186/1472-6947-12-8Search in Google Scholar

8. Forina, M. et al. (1991), “UCI machine learning repository - using chemical analysis determine the origin of wines”, available at: https://archive.ics.uci.edu/ml/datasets/Wine (01 January 2018).Search in Google Scholar

9. Guyon, I., Elisseeff, A. (2003), “An introduction to variable and feature selection”, Journal of machine learning research, Vol 3, No. 1, pp. 1157-1182.Search in Google Scholar

10. Kalousis, A., Prados, J., Hilario, M. (2007), “Stability of feature selection algorithms: a study on high-dimensional spaces”, Knowledge and information systems, Vol. 12, No. 1, pp. 95-116.10.1007/s10115-006-0040-8Search in Google Scholar

11. Kohavi R. (1995), “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection”, in Mellish, C. S. (Ed.), Artificial Intelligence Proceedings 14th International Joint Conference, Morgan Kaufmann, USA, pp. 1137-1145.Search in Google Scholar

12. Kuhn, M. (2017), “A short introduction to the caret package”, available at: https://cran.rproject.org/web/packages/caret/vignettes/caret.pdf (01 August 2017).Search in Google Scholar

13. Lichman, M. (2013), “UCI Machine Learning Repository”, available at: http://archive.ics.uci.edu/ml (01 February 2018).Search in Google Scholar

14. Robnik-Šikonja, M., Kononenko, I. (2003), “Theoretical and empirical analysis of ReliefF and RReliefF”, Machine learning, Vol. 53, No.1-2, pp. 23-69.10.1023/A:1025667309714Search in Google Scholar

15. Robnik-Šikonja, M., Savicky, P. (2017), “CORElearn - classification, regression, feature evaluation and ordinal evaluation”, R package version 1.51.2.Search in Google Scholar

16. Soundarapandian, P. (2015), “UCI machine learning repository - the chronic kidney disease prediction data set”, available at: https://archive.ics.uci.edu/ml/datasets/chronic_kidney_disease (01 January 2018).Search in Google Scholar

17. Wickham, H. (2009), ggplot2: Elegant Graphics for Data Analysis, Springer, New York.10.1007/978-0-387-98141-3Search in Google Scholar

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