Assessing Rough Classifiers

• Autorzy: Peters G.

Identyfikatory

DOI

10.3233/FI-2015-1191

• Języki publikacji: EN

Abstrakty

EN

Since its introduction a prime area of application of rough sets theory has been in the field of classification. In this area rough sets theory provides a powerful toolbox of methods to deal with incomplete and contradicting information. Obviously, the assessment of the obtained classification results is of crucial importance. In our paper, we propose and evaluate some rough performance indices to evaluated the quality of bi- and multinomial classifiers. To illustrate their characteristics we perform comparative experiments on a synthetically generated data set.

Słowa kluczowe

EN

rough sets; classification; performance indices; boundary roughness

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2015
• Tom: Vol. 137, nr 4
• Strony: 493--515
• Opis fizyczny: Bibliogr. 23 poz., tab., wykr.

Twórcy

autor

Peters G.

• Munich University of Applied Sciences Munich, Germany & Australian Catholic University, Australia

Bibliografia

• [1] Chen, S. Y.,Wang,W., Qu, G. F.: Traffic Incident Detection Based on Rough Sets Approach, 6. International Conference on Machine Learning and Cybernetics, 2007.
• [2] Grzymala-Busse, J.W.: Generalized Parameterized Approximations, 6th International Conference on Rough Sets and Knowledge Technology (RSKT 2011), 6954, 2011.
• [3] Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3. edition, Morgan Kaufmann, Waltham, MA, USA, 2011.
• [4] Jain, A. K.: Data clustering: 50 years beyond k-means, Pattern Recognition Letters, 31, 2010, 651–666.
• [5] Jain, A. K., Dubes, R. C.: Algorithms for clustering data, Prentice-Hall, Englewood Cliffs, NJ, USA, 1988.
• [6] Jain, A. K., Murty, M. N., Flynn, P. J.: Data Clustering: A Review, ACM Computing Surveys, 31(3), 1999, 264 – 323.
• [7] Kendall, M. G.: Discrimination and classification, Multivariate Analysis (P. R. Krishnaiah, Ed.), Academic Press, New York, 1966.
• [8] Kleinman, S., Busch, M. P., Hall, L., Thomson, R., Glynn, S., Gallahan, D., Ownby, H. E., Williams, A. E.: False-Positive HIV-1 Test Results in a Low-Risk Screening Setting of Voluntary Blood Donation, Journal of the American Medical Association, 280(12), 1998, 1080–1085.
• [9] Laplace, P. S.: Philosophical Essay on Probabilities, Dover Publications, New York, 1951.
• [10] Makhoul, J., Kubala, F., Schwartz, R., Weischedel, R.: Performance Measures For Information Extraction, Proceedings of DARPA Broadcast News Workshop, 1999.
• [11] Mitra, S.: An Evolutionary Rough Partitive Clustering, Pattern Recognition Letters, 25, 2004, 1439–1449.
• [12] Pawlak, Z.: Rough Sets, International Journal of Computer and Information Science, 11, 1982, 341–356.
• [13] Pawlak, Z.: Rough classification, International Journal of Man-Machine Studies, 20(5), 1984, 469–483.
• [14] Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, Netherlands, 1991.
• [15] Pawlak, Z.: Rough set theory and its applications, Journal of Telecommunications and Information Technology, 3, 2002, online.
• [16] van Rijsbergen, C. V.: Information Retrieval, Butterworth, London, Boston, MA, 1979.
• [17] Ślęzak, D., Ziarko,W.: The investigation of the Bayesian rough set model, International Journal of Approximate Reasoning, 40, 2005, 81–91.
• [18] Stefanowski, J.: On rough set based approaches to induction of decision rules, in: Rough Sets in Knowledge Discovery 1: Methodology and Applications (L. Polkowski, A. Skowron, Eds.), Physica-Verlag, Heidelberg, 1998, 500–529.
• [19] Witten, I. H., Frank, E., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques, 3. edition, Morgan Kaufmann, Burlington,MA, USA, 2011.
• [20] Xua, B., Zhoua, Y., Lua, H.: An improved accuracy measure for rough sets, Journal of Computer and System Sciences, 71(2), 2005, 163–173.
• [21] Yao, Y. Y.: Probabilistic approaches to rough sets, Expert Systems, 20(5), 2003, 287–297.
• [22] Yao, Y. Y.: Probabilistic rough set approximations, International Journal of Approximate Reasoning, 49, 2008, 255–271.
• [23] Ziarko,W.: Probabilistic approach to rough sets, International Journal of Approximate Reasoning, 49, 2008, 272–284.