Abstract
A new version of the Belief SEEKER software that incorporates some aspects of rough set theory is discussed in this paper. The new version is capable of generating certain belief networks (for consistent data) and possible belief networks (for inconsistent data). Then, both types of networks can be readily converted onto respective sets of production rules, which includes both certain and/or possible rules. The new version or broadly speaking-methodology, was tested in mining the melanoma database for the best descriptive attributes of skin illness. It was found, that both types of knowledge representation, can be readily used for classification of melanocytic skin lesions.
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Grzymała-Busse, J.W., Hippe, Z.S.: A Search for the Best Data Mining Method to Predict Melanoma. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 538–545. Springer, Heidelberg (2002)
Chmielewski, M.R., Grzymała-Busse, J.W., Peterson, N.W., Than, S.: The Rule Induc-tion System LERS - A Version for Personal Computers. Foundations of Computing and Dec. Sciences 18(3-4), 181–211 (1993)
Błajdo, P., Grzymała-Busse, J.W., Hippe, Z.S., Knap, M., Marek, T., Mroczek, T., Wrzesień, M.: A suite of machine learning tools for knowledge extraction from data. In: Tadeusiewicz, R., Ligęza, A., Szymkat, M. (eds.) Computer Methods and Systems in Scientific Research, Edition of ”Oprogr. Naukowe”, Cracow, pp. 479–484 (2003) (in Polish)
Hippe, Z.S., Mroczek, T.: Melanoma classification and prediction using belief networks. In: Kurzynski, M., Puchała, E., Wozniak, M. (eds.) Computer Recognition Systems, Wrocław University of Technology Edit. Office, Wrocław, pp. 337–342 (2003)
Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordercht (1991)
Cooper, G.F., Herskovits, E.: A Byesian method for the induction of probabilistic networks from data. Machine Learning (9), 309–347 (1992)
Andrews, R., Bajcar, S., Grzymała-Busse, J.W., Hippe, Z.S., Whiteley, C.: Optimization of the ABCD Formula for Melanoma Diagnosis Using C4.5, a Data Mining System. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 630–636. Springer, Heidelberg (2004)
Lorentzen, H., Weissman, K., Secher, L., Peterson, C.S., Larsen, F.G.: The dermatoscopic ABCD rule does not improve diagnostic accuracy of malignant melanoma. Acta Derm. Venereol 79, 469–472 (1999)
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Mroczek, T., Grzymała-Busse, J.W., Hippe, Z.S. (2004). Rules from Belief Networks: A Rough Set Approach. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_58
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DOI: https://doi.org/10.1007/978-3-540-25929-9_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
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