References
- Abraham, A., Jain, R., Thomas, J. and Han, S.Y. (2007). D-SCIDS: Distributed soft computing intrusion detection system, Journal of Network and Computer Applications 30(1): 81-98, DOI: 10.1016/j.jnca.2005.06.001.10.1016/j.jnca.2005.06.001
- Bache, K. and Lichman, M. (2013). UCI Machine Learning Repository, University of California, Irvine, CA, http://archive.ics.uci.edu/ml
- Bazan, J.G., Szczuka, M.S. and Wróblewski, J. (2002). A new version of Rough Set Exploration System, in J.J. Alpigini et al. (Eds.), Rough Sets and Current Trends in Computing, Lecture Notes in Computer Science, Vol. 2475, Springer, Berlin/Heidelberg, pp. 397-404, DOI: 10.1007/3-540-45813-1_52.10.1007/3-540-45813-1_52
- Błaszczyński, J., Greco, S., Matarazzo, B., Słowiński, R. and Szeląg,M. (2013). jMAF-Dominance-based rough set data analysis framework, in A. Skowron and Z. Suraj (Eds.), Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam, Springer, Berlin/Heidelberg, pp. 185-209, DOI: 10.1007/978-3-642-30344-9_5.10.1007/978-3-642-30344-9_5
- Borowik, G. (2013). Boolean function complementation based algorithm for data discretization, in R. Moreno-Díaz et al. (Eds.), Computer Aided Systems Theory, Lecture Notes in Computer Science, Vol. 8112, Springer, Berlin/Heidelberg, pp. 218-225, DOI: 10.1007/978-3-642-53862-9_28.10.1007/978-3-642-53862-9_28
- Borowik, G. and Kowalski, K. (2015). Rule induction based on frequencies of attribute values, Proceedings of SPIE: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 9662: 96623R, DOI: 10.1117/12.2205899.
- Borowik, G., Kowalski, K. and Jankowski, C. (2015a). Novel approach to data discretization, Proceedings of SPIE: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 9662: 96623U, DOI: 10.1117/12.2205916.
- Borowik, G., Kraśniewski, A. and Łuba, T. (2015b). Rule induction based on logic synthesis methods, in H. Selvaraj et al. (Eds.), Progress in Systems Engineering, Advances in Intelligent Systems and Computing, Vol. 330, Springer International Publishing, Cham, pp. 813-816, DOI: 10.1007/978-3-319-08422-0_118.10.1007/978-3-319-08422-0_118
- Borowik, G. and Łuba, T. (2014). Fast algorithm of attribute reduction based on the complementation of Boolean function, in R. Klempous et al. (Eds.), Advanced Methods and Applications in Computational Intelligence, Topics in Intelligent Engineering and Informatics, Springer International Publishing, Cham, pp. 25-41, DOI: 10.1007/978-3-319-01436-4_2.10.1007/978-3-319-01436-4_2
- Brayton, R.K., Hachtel, G.D., McMullen, C.T. and Sangiovanni-Vincentelli, A. (1984). Logic Minimization Algorithms for VLSI Synthesis, Kluwer Academic Publishers, Dordrecht, DOI: 10.1007/978-1-4613-2821-6.10.1007/978-1-4613-2821-6
- Brzozowski, J.A. and Łuba, T. (1997). Decomposition of Boolean functions specified by cubes, Research report CS- 97-01, University of Waterloo, Waterloo.
- Jankowski, C., Reda, D., Mańkowski, M. and Borowik, G. (2015). Discretization of data using Boolean transformations and information theory based evaluation criteria, Bulletin of the Polish Academy of Sciences: Technical Sciences 63(4): 923-932, DOI: 10.1515/bpasts-2015-0105.10.1515/bpasts-2015-0105
- Komorowski, J., Pawlak, Z., Polkowski, L. and Skowron, A. (1999). Rough sets: A tutorial, https://eecs.ceas.uc.edu/~mazlack/dbm.w2011/Komorowski.RoughSets.tutor.pdf.
- Korzen, M. and Jaroszewicz, S. (2005). Finding reducts without building the discernibility matrix, Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, ISDA’05, Wrocław, Poland, pp. 450-455, DOI: 10.1109/ISDA.2005.45.10.1109/ISDA.2005.45
- Liu, G., Li, L., Yang, J., Feng, Y. and Zhu, K. (2015). Attribute reduction approaches for general relation decision systems, Pattern Recognition Letters 65: 81-87, DOI: 10.1016/j.patrec.2015.06.031.10.1016/j.patrec.2015.06.031
- Liu, H. and Setiono, R. (1997). Feature selection via discretization, IEEE Transactions on Knowledge and Data Engineering 9(4): 642-645, DOI: 10.1109/69.617056.10.1109/69.617056
- Łuba, T., Borowik, G., Kraśniewski, A., Rutkowski, P. and Ługowska, I. (2014). Application of logic synthesis algorithms for data mining in medical databases, 9th International Seminar Statistics and Clinical Practice, Warsaw, Poland, pp. 36-39.
- Łuba, T. and Rybnik, J. (1992). Intelligent decision support: Handbook of applications and advances of the rough sets theory, in S.Y. Huang (Ed.), Rough Sets and Some Aspects of Logic Synthesis, Springer Netherlands, Dordrecht, pp. 181-199, DOI: 10.1007/978-94-015-7975-9_13.10.1007/978-94-015-7975-9_13
- Martinović, G., Bajer, D. and Zorić, B. (2014). A differential evolution approach to dimensionality reduction for classification needs, International Journal of Applied Mathematics and Computer Science 24(1): 111-122, DOI: 10.2478/amcs-2014-0009.10.2478/amcs-2014-0009
- Min, F., Hu, Q. and Zhu, W. (2014). Feature selection with test cost constraint, International Journal of Approximate Reasoning 55(1Pt2): 167-179, DOI: 10.1016/j.ijar.2013.04.003.10.1016/j.ijar.2013.04.003
- Nguyen, H.S. (2006). Approximate Boolean reasoning: Foundations and applications in data mining, in J.F. Peters and A. Skowron (Eds.), Transactions on Rough Sets V, Lecture Notes in Computer Science, Vol. 4100, Springer, Berlin/Heidelberg, pp. 334-506, DOI: 10.1007/11847465_16.10.1007/11847465_16
- Petrick, S.R. (1956). A direct determination of the irredundant forms of a Boolean function from the set of prime implicants, Technical report AFCRC-TR-56-110, Air Force Cambridge Research Center, Cambridge, MA.
- Predki, B., Słowiński, R., Stefanowski, J., Susmaga, R. and Wilk, S. (1998). ROSE-software implementation of the rough set theory, in L. Polkowski and A.
- Skowron (Eds.), Rough Sets and Current Trends in Computing, Springer, Berlin/Heidelberg, pp. 605-608, DOI: 10.1007/3-540-69115-4_85.10.1007/3-540-69115-4_85
- Predki, B. andWilk, S. (1999). Rough set based data exploration using ROSE system, in Z.W. Raś and A. Skowron (Eds.), Foundations of Intelligent Systems: 11th International Symposium, Springer, Berlin/Heidelberg, pp. 172-180, DOI: 10.1007/BFb0095102.10.1007/BFb0095102
- Sasao, T. (2011). Index generation functions: Recent developments, 41st IEEE International Symposium on Multiple-Valued Logic, Tuusula, Finland, DOI: 10.1109/ISMVL.2011.17.10.1109/ISMVL.2011.17
- Sasao, T. (2015). Index generation functions, EPFL Workshop on Logic Synthesis & Verification, Lausanne, Switzerland.
- Skowron, A. and Rauszer, C. (1992). The discernibility matrices and functions in information systems, in R. Słowiński (Ed.), Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht, pp. 331-362.
- Stefanowski, J., Krawiec, K. andWrembel, R. (2017). Exploring complex and big data, International Journal of Applied Mathematics and Computer Science 27(4): 669-679, DOI: 10.1515/amcs-2017-0046.10.1515/amcs-2017-0046
- Steinbach, B. and Posthoff, C. (2012). Improvements of the construction of exact minimal covers of Boolean functions, in R. Moreno-Díaz et al. (Eds.), Computer Aided Systems Theory-EUROCAST 2011, Lecture Notes in Computer Science, Vol. 6928, Springer, Berlin/Heidelberg, pp. 272-279, DOI: 10.1007/978-3-642-27579-1_35.10.1007/978-3-642-27579-1_35
- Steinbach, B. and Posthoff, C. (2013). Fast calculation of exact minimal unate coverings on both the CPU and the GPU, in R. Moreno-Díaz et al. (Eds.), Computer Aided Systems Theory, Springer, Berlin/Heidelberg, pp. 234-241, DOI: 10.1007/978-3-642-53862-9_30.10.1007/978-3-642-53862-9_30
- Su, M.-Y., Yu, G.-J. and Lin, C.-Y. (2009). A real-time network intrusion detection system for large-scale attacks based on an incremental mining approach, Computers & Security 28(5): 301-309, DOI: 10.1016/j.cose.2008.12.001.10.1016/j.cose.2008.12.001
- Sun, L., Xu, J. and Li, Y. (2014). A feature selection approach of inconsistent decision systems in rough set, Journal of Computers 9(6): 1333-1340, DOI: 10.4304/jcp.9.6.1333-1340.10.4304/jcp.9.6.1333-1340
- Szemenyei, M. and Vajda, F. (2017). Dimension reduction for objects composed of vector sets, International Journal of Applied Mathematics and Computer Science 27(1): 169-180, DOI: 10.1515/amcs-2017-0012.10.1515/amcs-2017-0012
- Zhong, N. and Skowron, A. (2001). A rough set-based knowledge discovery process, International Journal of Applied Mathematics and Computer Science 11(3): 603-619.