References
- Agrahari, S. and Singh, A.K. (2022). Adaptive PCA-based feature drift detection using statistical measure, Cluster Computing 25(6): 4481–4494.
- Baena-Garcia, M., Campo-Avila, J., Bifet, A., Gavald, R. and Morales-Bueno, R. (2006). Early drift detection, in A.L.C. Bazzan and S. Labidi (Eds), Advances in Artificial Intelligence, Lecture Notes Artificial Intelligence, Vol. 3171, Springer, Berlin/Handelberg, pp. 286–295.
- Bartz-Beielstein, T. and Lukas, H. (2024). Drift detection and handling, in E. Bartz and T. Bartz-Beielstein (Eds), Online Machine Learning: A Practical Guide with Examples in Python, Springer Nature Singapore, Singapore, pp. 23–39.
- Bifet, A. and Gavaldà, R. (2009). Adaptive learning from evolving data streams, Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII (IDA’09), Lyon, France, pp. 249–260.
- Bifet, A., Holmes, G., Kirkby, R. and Pfahringer, B. (2010). MOA: Massive online analysis, Journal of Machine Learning Research 11(2010): 1601–1604.
- Blackard, J. (1998). Covertype, Dataset, UCI Machine Learning Repository, DOI: 10.24432/C50K5N.
- Breiman, L. (2001). Random forests, Machine Learning 45(2001): 5–32.
- Chicco, D., Tötsch, N. and Jurman, G. (2021). The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation, Bio-Data Mining 14(13): 1–22.
- Dhal, P. and Alad, C. (2022). A comprehensive survey on feature selection in the various fields of machine learning, Applied Intelligence 52(2022): 4543–4581.
- de Souza, V.M.A., dos Reis, D.M., Maletzke, A.G. and Batista, G.E.A.P.A. (2020). Challenges in benchmarking stream learning algorithms with real-world data, Data Mining and Knowledge Discovery 34(2020): 1805–1858.
- Duda, P., Przybyszewski, K. and Wang, L. (2020). A novel drift detection algorithm based on features’ importance analysis in a data streams environment, Journal of Artificial Intelligence and Soft Computing Research 10(4): 287–298.
- Efron, B., Hastie, T., Johnstone, I. and Tibshirani, R. (2004). Least angle regression, The Annals of Statistics 32(2): 407–451.
- Fernández-Delgado, M., Cernadas, E., Barro, S. and Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems?, Journal of Machine Learning Research 15(1): 3133–3181.
- Frank, A. (2010). UCI Machine Learning Repository, University of California, Irvine, http://archive.ics.uci.edu/ml.
- Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A. (2014). A survey on concept drift adaptation, ACM Computing Surveys 46(2014): 1–37.
- Gonçalves, P.M., de Carvalho Santos, S.G., Barros, R.S. and Vieira, D.C. (2014). A comparative study on concept drift detectors, Expert Systems with Applications 41(18): 8144–8156.
- Guo, H., Li, H., Ren, Q. and Wang, W. (2022). Concept drift type identification based on multi-sliding windows, Information Sciences 585(2022): 1–23.
- Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection, Journal of Machine Learning Research 3: 1157–1182.
- Japkowicz, N. and Boukouvalas, Z. (2024). Machine Learning Evaluation: Towards Reliable and Responsible AI, Cambridge University Press, Cambridge.
- Mielniczuk, J. and Wawrzeńczyk, A. (2025). Accounting for label shift of positive unlabeled data under selection bias, International Journal of Applied Mathematics and Computer Science 35(3): 507–517, DOI: 10.61822/amcs-2025-0036.
- Montiel, J., Read, J., Bifet, A. and Abdessalem, T. (2018). Scikit-multiflow: A multi-output streaming framework, Journal of Machine Learning Research 19(72): 1–5.
- Porwik, P. and Doroz, R. (2021). Adaptation of the idea of concept drift to some behavioral biometrics: Preliminary studies, Engineering Applications of Artificial Intelligence 99(2021): 104135.
- Saeys, S., Inza, I. and Larrañaga, P. (2007). A review of feature selection methods for machine learning, Bioinformatics 23(19): 2507–2517.
- Stapor, K., Ksieniewicz, P., García, S. and Woźniak, M. (2021). How to design the fair experimental classifier evaluation, Applied Soft Computing 104(2021): 107219.
- Stefanowski, J., Krawiec, K. and Wrembel, R. (2017). Exploring complex and big data, International Journal of Applied Mathematics and Computer Science 27(4): 669–679, DOI: 10.1515/amcs-2017-0046.
- Tavallaee, M., Bagheri, E., Lu, W. and Ghorbani, A.A. (2009). A detailed analysis of the KDD CUP 99 data set, Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, Canada, pp. 1–6.
- Usman, M., Sher, M. and Khan, M.N.A. (2023). A survey on feature selection techniques based on filtering criteria, Information 14(3): 191.
- Yamada, M., Jitkrittum, W., Sigal, L., Xing, E.P. and Sugiyama, M. (2014). High-dimensional feature selection by feature-wise kernelized Lasso, Neural Computation 26(1): 185–207.
- Zhao, D. and Koh, Y.-S. (2020). Feature drift detection in evolving data streams, in S. Hartmann et al. (Eds), Database and Expert Systems Applications (DEXA 2020), Lecture Notes in Computer Science, Vol. 12392, Springer, Cham, pp. 319–328.
- Zhu, Q. (2020). On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset, Pattern Recognition Letters 136(2020): 71–80.
- Žliobaitė, I. (2010). Learning under concept drift: An overview, arXiv 1010.4784.