Abstract
In this work we exploit the effects of applying methods for constructions of granular reflections of decision systems developed up to now in the framework of rough mereology, along with kernel methods for the building of classifiers. In this preliminary report we present results obtained with the SVM classification with use of the RBF kernel. The approximation metod we use is the optimized \(\varepsilon \) concept dependent granulation. We experimentally verify the validity of this new approach with test data: Wisconsin Diagnostic Breast Cancer, Fertility Diagnosis, Parkinson Disease and the Prognostic Wisconsin Breast Cancer Database. The results are very promising as the obtained accuracy is not diminished but the size of the granular decision system is radically diminished.
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References
Artiemjew, P.: On strategies of knowledge granulation and applications to decision systems, Ph.D. Dissertation, Polish Japanese Institute of Information Technology. L. Polkowski, Supervisor, Warsaw (2009)
Artiemjew, P.: A review of the knowledge granulation methods: discrete vs continuous algorithms. In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems. ISRL, vol. 43, pp. 41–59. Springer, Heidelberg (2013)
Boser, B.E., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Proceedings of Vth International Workshop on Computational Learning Theory, pp. 144–152. ACM Press (1992)
Chang, C.-C., Lin, C.-J.: LIBSVM, a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm
Polkowski, L.: Formal granular calculi based on rough inclusions (a feature talk). In: Proceedings 2005 IEEE International Conference on Granular Computing GrC 2005, pp. 57–62. IEEE Press (2005)
Polkowski, L.: Granulation of knowledge in decision systems: the approach based on rough inclusions. The method and its applications. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 69–79. Springer, Heidelberg (2007)
Polkowski, L.: Formal granular calculi based on rough inclusions (a feature talk). In: Proceedings 2006 IEEE International Conference on Granular Computing GrC 2006, pp. 57–62. IEEE Press (2006)
Polkowski, L.: Approximate Reasoning by Parts. An Introduction to Rough Mereology. ISRL, vol. 20. Springer, Heidelberg (2011)
Polkowski, L., Artiemjew, P.: Granular Computing in Decision Approximation - An Application of Rough Mereology. ISRL, vol. 77. Springer, Switzerland (2015). ISBN 978-3-319-12879-5, pp. 1–422
Acknowledgement
The author wishes to thank Professor Lech Polkowski for kind help and advice. The research has been supported by grant 1309-802 from Ministry of Science and Higher Education of the Republic of Poland.
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Szypulski, J., Artiemjew, P. (2015). The Rough Granular Approach to Classifier Synthesis by Means of SVM. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_23
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DOI: https://doi.org/10.1007/978-3-319-25783-9_23
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