References
- 1. Breiman L., (1995), “Better Subset Regression Using the Nonnegative Garrote”, Technometrics, Vol. 37 No 4, pp. 373 – 384.
- 2. Breiman L., (1992), “The Little Bootstrap and Other Methods for Dimensionality Selection in Regression: X-fixed Prediction Error”, Journal of American Statistical Association, Vol. 87 No. 419, pp. 738-754.
- 3. Breiman, L. (1996), “Bagging predictors”, Machine Learning. 24 (2), pp. 123–140.
- 4. Grubinger, T., Zeileis, A. and Pfeiffer, K., 2014. Evtree: Evolutionary learning of globally optimal classification and regression trees in R. J. Stat. Software 61 (1), pp. 1-29.
- 5. Efron B., (1979), “Bootstrap Methods: Another Look at the Jackknife”, the Annals of Statistics, Vol. 17, pp. 1–26.
- 6. Efron B., Tibshirani R., (1997), “Improvements on Cross-Validation: The.632+ Bootstrap Method”, Journal of the American Statistical Association, vol. 92, pp. 548–560.
- 7. Hoerl E., Kennard W., (1970), “Ridge Regression. Applications to nonorthogonal Problems”, Technometrics, Vol. 12 No. 1, pp. 69-82. Iz 2012
- 8. Iannarilli F., Rubin P., (2003), Feature selection for multiclass discrimination via mixed-integer linear programming, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25 No. 6, pp. 779-783.
- 9. James G., D. W., Hastie T., Tibshirani R., (2013), An Introduction to Statistical Learning. Springer, STS Vol. 103.
- 10. Kim B., Shin S., (2019), “Principal weighted logistic regression for sufficient dimension reduction in binary classification”, Journal of the Korean Statistical Society, Vol. 48 No. 2, pp. 194-206.
- 11. Krstajic D., Buturovic J., Leahy E., Thomas S., (2014), “Cross-validation pitfalls when selecting and assessing regression and classification models”, Cheminformatics, Vol. 6 Article No. 10.
- 12. MacKinnon J., (2002), “Bootstrap Inference in Econometrics”, The Canadian Journal of Economics, Vol. 35 No. 4, pp. 615—645.
- 13. Maldonado S., Pérez J., Weber R., Labbé M., (2014), Feature Selection for Support Vector Machines via Mixed Integer Linear Programming, Information Sciences, Vol. 279, pp. 163–175.
- 14. Pampel F., (2000), Logistic regression: A primer. Sage University Papers Series on Quantitative Applications in the Social Sciences, 07-132. Sage Publications, Thousand Oaks, CA.
- 15. Pedregosa et al., (2011), Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research 12, pp. 2825-2830.
- 16. Velliangiri S., Alagumuthukrishnan S., Joseph S., (2019), A Review of Dimensionality Reduction Techniques for Efficient Computation, Procedia Computer Science, Vol. 165, pp. 104-111.
- 17. Vrigazova B., (2018), “Nonnegative Garrote as a Variable Selection Method in Panel Data”, International Journal of Computer Science and Information Security, Vol. 16 No. 1.
- 18. Vrigazova B., Ivanov I., (2019), “Optimization of the ANOVA Procedure for Support Vector Machines”, International Journal of Recent Technology and Engineering, Vol. 8 No. 4.
- 19. Vrigazova B., Ivanov I., (2020a), “The bootstrap procedure in classification problems”, International Journal of Data Mining, Modelling and Management, Vol. 12 No. 4.
- 20. Vrigazova, B.& Ivanov, I., (2020b), “Tenfold bootstrap procedure for support vector machines”, Computer Science, Vo. 21 No. 2, pp. 241-257. 10.7494/csci.2020.21.2.3634.
- 21. Wong T., (2015), “Performance evaluation of classification algorithms by k-fold and leave-one-out cross-validation”, Pattern Recognition, Vol. 48 No. 9, pp. 2839–2846.
- 22. Yeturu К., (2020), Chapter 3 - Machine learning algorithms, applications, and practices in data science, Editor(s): Arni S.R. Srinivasa Rao, C.R. Rao, Handbook of Statistics, Elsevier, Vol. 43, pp. 81-206.