Optimal solution of a decision table: a rough set based software toolkit

• Autorzy: Shrabonti G. , Alam S. S.
• Języki publikacji: EN

Abstrakty

EN

As the amount of information in the world is steadily increasing, there is a growing demand for tools to analyze information. Rough set methodology is very useful in data analysis of an information system such as in data generalization and data reduction. In this paper, we generalize the method proposed by Pawlak for reduction of a special and important class of an information system called decision table, where the attribute values are almost identical, not exactly equal. After simplification of decision table we obtain minimal solution (not necessarily unique) from a set of input data considering that they are almost equal. As the simplification of a decision table does not yield unique result, so, in general, many minimal solutions are possible and making choice of 'best' or 'optimal' solution from amongst them is not an easy task. The 'best' or 'optimal' criteria are different for different decision makers depending on their intensions. In this paper, a software toolkit (based on rough set theory) has been proposed for finding optimal solution(s) from amongst all possible minimal solutions of a decision table. As significance of attributes plays a very important role in rough set analysis, the proposed method for getting optimal solution(s) has been developed by using the measure of significance of attributes.

Słowa kluczowe

EN

decision table; optimal solution; software tool

PL

tablica decyzyjna; rozwiązanie optymalne

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2004
• Tom: Vol. 29, No. 3
• Strony: 227--245
• Opis fizyczny: Bibliogr. 16 poz.

Twórcy

autor

Shrabonti G.

• Department of Mathematics, Indian Institute of Technology, Kharagpur - 721302, India

autor

Alam S. S.

• Department of Mathematics, Indian Institute of Technology, Kharagpur - 721302, India

Bibliografia

• [1] Duntsch, I. and Gediga, G., Uncertainty Measures of Rough Set Prediction, Artificial Intelligence, 106(1998), 109 - 137.
• [2] Duntsch, I. and Gediga, G., The Rough Set Engine GROBIAN, Proceedings of the 15th MACS World Congress, Vol.4 , Berhn, August 1997.
• [3] Gawrys, M. and Sienkiewicz, J., Rough Set Library User's Manual (Version 2.0, September 1993) (Institute of Computer Science, Warsaw University of Technology), http://www.coit.uncc.edu/~ras/manual.pdf.
• [4] Gediga, G. and Duntsch, I., Rough Approximation Quality Revisited, Artificial Intelligence, 132(2001), 219 - 234.
• [5] Goebel, M. and Gruenwald, L., A Survey of Data Mining and Knowlege Discovery Software Tools, ACM SIGKDD Explorations 1, 1(1999), 20 - 33.
• [6] Ghosh, S. and Alam, S. S., (a, p) Reduction of Decision Table: A Rough Approach, Foundations of Computing and Decision Sciences, Vol.26, 1,(2001), 273 - 281.
• [7] Ghosh, S. and Alam, S. S., Approximate Decision Logic and Reduction of Decision Rules, Foundations of Computing and Decision Sciences, Vol.28, 1,(2003), 3-16.
• [8] Mienko, R., Słowiński, R., Stefanowski, J., and Susmaga, R., Rough Family -Software Implementation of Rough Set Based Data Analysis and Rule Discovery Techniques, Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets and Machine Discovery, Tokyo, November 6 - 8, 1996, 437 - 440.
• [9] Ohrn, A., Komorowski, J., Skowron, A., and Synak, R, The ROSETTA Software System, Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Vol. 19 of Studies in Fuzziness and Soft Computing, Physica-Verlag, Heidelberg, Germany, 1998, 572 - 576.
• [10] Pawlak, Z., Rough Sets, Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, 1991.
• [11] Pawlak, Z., Rough Sets and Intelligent Data Analysis, extended version of the paper presented at the Konan- IIAS A Joint Workshop on Natural Environment Management and Applied Systems Analysis held September 6 - 9, 2000 in Laxemburg, Austria.
• [12] Predki, B., Slowiński, R., Stefanowski, J., Susmaga, R., and Wilk, S., ROSE - Software Implementation of the Rough Set theory In: Polkowski, L. and Skowron, A. (eds.) Rough Sets and Current Trends in Computing, Proceedings of the RSCTC’98 Conference, Poland, June 22 - 26, Lecture Notes in A rtificial Intelligence 1424, Springer Verlag, 1998, 605 - 608.
• [13] ROSETTA: http://www.idi.ntnu.no/~aleks/rosetta/.
• [14] ROUGH ENOUGH: http://www.trolldata.no/renough/.
• [15] Stepaniuk, J., Optimizations of Rough Set Model, Fundamenta Informaticae, 35(1998), 1 - 19.
• [16] Wilk, S., Fhnkman, M., Michałowski, W., Nilsson, S., Slowiński, R., and Susmaga, R., Identification of Biodiversity and other Forest Attributes for Sustainable Forest Management: Siberian Forest Case Study, IIASA Interim Report IR - 98 - 106/ December.