Application of Granular Computing Paradigm in Knowledge Induction

Authors: Iftikhar U. Sikder

Abstract:

This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: Concept approximation, granular computing, reducts, rough set theory, rule induction.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132435

References:

[1] A. Bargiela and W. Pedrycz, Granular computing: an introduction vol. 717: Springer Science & Business Media, 2012.
[2] T. Y. Lin, Y. Y. Yao, and L. A. Zadeh, Data mining, rough sets and granular computing vol. 95: Physica, 2013.
[3] Y. Yao, "A triarchic theory of granular computing," Granular Computing, vol. 1, pp. 145-157, 2016.
[4] L. Livi and A. Sadeghian, "Granular computing, computational intelligence, and the analysis of non-geometric input spaces," Granular Computing, vol. 1, pp. 13-20, 2016.
[5] J. T. Yao, A. V. Vasilakos, and W. Pedrycz, "Granular computing: perspectives and challenges," IEEE Transactions on Cybernetics, vol. 43, pp. 1977-1989, 2013.
[6] Z. Pawlak and A. Skowron, "Rudiments of rough sets," Information sciences, vol. 177, pp. 3-27, 2007.
[7] Z. Pawlak and A. Skowron, "Rough sets: some extensions," Information sciences, vol. 177, pp. 28-40, 2007.
[8] I. U. Sikder, "Rough Sets and Granular Computing in Geospatial Information," in Handbook of Research on Geoinformatics, ed: IGI Global, 2009, pp. 154-159.
[9] C. H. Aldridge, "A Theoretical Foundation for Geographic Knowledge Discovery in Databases," in First International Conference on Geographic Information Science, Georgia, USA, 2000.
[10] I. U. Sikder and A. Gangopadhyay, "Distributed Data Warehouse for Go-spatial Services," in ERP & Data Warehouse in Oganizations: Issues and Challenges, G. Grant, Ed., ed: IRM Press, 2003, pp. 132-145.
[11] I. U. Sikder, "A variable precision rough set approach to knowledge discovery in land cover classification," International Journal of Digital Earth, vol. 9, pp. 1206-1223, 2016.
[12] I. Düntsch and G. Gediga, "Rough set data analysis--A road to non-invasive knowledge discovery," 2000.
[13] J. T. Yao and N. Azam, "Web-Based Medical Decision Support Systems for Three-Way Medical Decision Making With Game-Theoretic Rough Sets," Ieee Transactions on Fuzzy Systems, vol. 23, pp. 3-15, Feb 2015.
[14] I. U. Sikder and A. Gangopadhyay, "Managing uncertainty in location services using rough set and evidence theory," Expert Systems with Applications, vol. 32, pp. 386-396, Feb 2007.
[15] I. U. Sikder and T. Munakata, "Application of rough set and decision tree for characterization of premonitory factors of low seismic activity," Expert Systems with Applications, vol. 36, pp. 102-110, 2009.
[16] S. Mal-Sarkar, I. U. Sikder, and V. K. Konangi, "Spatio-temporal pattern discovery in sensor data: a multivalued decision systems approach," Knowledge-Based Systems, vol. 109, pp. 137-146, 2016.
[17] J. W. Grzymala-Busse and S. Siddhaye, "Rough set approaches to rule induction from incomplete data," in Proceedings of the IPMU, 2004, pp. 923-930.
[18] B. Prędki and S. Wilk, "Rough set based data exploration using ROSE system," in Foundations of Intelligent Systems: 11th International Symposium, ISMIS’99 Warsaw, Poland, June 8–11, 1999 Proceedings, Z. W. Raś and A. Skowron, Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 1999, pp. 172-180.
[19] J. Stefanowski and S. Wilk, "Improving rule based classifiers induced by MODLEM by selective pre-processing of imbalanced data," in Proc. of the RSKD Workshop at ECML/PKDD, Warsaw, 2007, pp. 54-65.
[20] J. Stefanowski, "The rough set based rule induction technique for classification problems," in In Proceedings of 6th European Conference on Intelligent Techniques and Soft Computing EUFIT, 1998.
[21] "Breast Cancer Wisconsin (Original) Data Set," W. H. Wolberg, Ed., ed. UCI Machine Learning Repository, 1992.
[22] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, vol. 16, pp. 321-357, 2002.
[23] T. Zhu, Y. Lin, and Y. Liu, "Synthetic minority oversampling technique for multiclass imbalance problems," Pattern Recognition, 2017.
[24] U. Fayyad and K. Irani, "Multi-interval discretization of continuous-valued attributes for classification learning," 1993.
[25] A. Chouchoulas and Q. Shen, "Rough set-aided keyword reduction for text categorization," Applied Artificial Intelligence, vol. 15, pp. 843-873, 2001.