Abstract
This paper is devoted to an exposition of the idea of using granular structures obtained from data in the classification tasks of these data into decision classes. Classifiers are induced from granular reflections of data sets.
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Artiemjew, P.: Classifiers from granulated data sets: Concept dependent and layered granulation. In: Rough Sets in Knowledge Discovery RSKD 2007. Workshop at European Conference on Machine Learning/International Conference on Principles of Knowledge Discovery in Data ECML/PKDD 2007, pp. 1–9. Warsaw Univ. Press, Warsaw (2007)
Artiemjew, P.: On classification of data by means of rough mereological granules of objects and rules. In: RSKT 2008. LNCS (LNAI) (in print, 2008)
Artiemjew, P.: Rough mereological classifiers obtained from weak rough inclusions. In: RSKT 2008. LNCS (LNAI) (in print, 2008)
Bazan, J.G.: A comparison of dynamic and non–dynamic rough set methods for extracting laws from decision tables. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, vol. 1, pp. 321–365. Physica Verlag, Heidelberg (1998)
Nguyen, S.H.: Regularity analysis and its applications in Data Mining. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 289–378. Physica Verlag, Heidelberg (2000)
Polkowski, L.: Formal granular calculi based on rough inclusions (a feature talk). In: Hu, X., Liu, Q., Skowron, A., Lin, T.Y., Yager, R.R., Zhang, B. (eds.) IEEE GrC 2005, pp. 57–62. IEEE Press, Piscataway (2005)
Polkowski, L.: Formal granular calculi based on rough inclusions (a feature talk). In: Zhang, Y.-Q., Lin, T.Y. (eds.) IEEE GrC 2006, pp. 9–18. IEEE Press, Piscataway (2006)
Polkowski, L.: On the idea of using granular rough mereological structures in classification of data. In: RSKT 2008. LNCS (LNAI), vol. 5009, Springer, Heidelberg (in print, 2008)
Polkowski, L., Artiemjew, P.: On granular rough computing: Factoring clasifiers through granular structures. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 280–290. Springer, Heidelberg (2007)
UCI Repository, http://www.ics.uci.edu/mlearn/~databases/
Wróblewski, J.: Adaptive aspects of combining approximation spaces. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough Neural Computing, pp. 139–156. Springer, Berlin (2004)
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Polkowski, L. (2008). On the Idea of Using Granular Rough Mereological Structures in Classification of Data. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_32
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DOI: https://doi.org/10.1007/978-3-540-79721-0_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79720-3
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