Abstract
Manually evaluating important and interesting rules generated from data is generally infeasible due to the large number of rules extracted. Different approaches such as rule interestingness measures and rule quality measures have been proposed and explored previously to extract interesting and high quality association rules and classification rules. Rough sets theory was originally presented as an approach to approximate concepts under uncertainty. In this paper, we explore rough sets based rule evaluation approaches in knowledge discovery. We demonstrate rule evaluation approaches through a real-world geriatric care data set from Dalhousie Medical School. Rough set based rule evaluation approaches can be used in a straightforward way to rank the importance of the rules. One interesting system developed along these lies in HYRIS (HYbrid Rough sets Intelligent System). We introduce HYRIS through a case study on survival analysis using the geriatric care data set.
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References
Pawlak, Z.: Rough Sets. In: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht (1991)
Li, J., Cercone, N.: Introducing A Rule Importance Measure. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, Springer, Heidelberg (2006)
Li, J., Cercone, N.: Discovering and Ranking Important Rules. In: Proceedings of IEEE International Conference on Granular Computing, vol. 2, Beijing, China, 25-27 July, 2005, pp. 506–511. IEEE, Los Alamitos (2005)
Pattaraintakorn, P., Cercone, N., Naruedomkul, K.: Hybrid Intelligent Systems: Selecting Attributes for Soft-Computing Analysis. In: Proc. of the 29th Annual International Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 319–325 (2005)
Kryszkiewicz, M., Rybinski, H.: Finding Reducts in Composed Information Systems, Rough Sets, Fuzzy Sets Knowldege Discovery. In: Ziarko, W.P. (ed.) Proceedings of the International Workshop on Rough Sets, Knowledge Discovery, pp. 261–273. Springer, Heidelberg (1994)
Bazan, J., et al.: Rough set algorithms in classification problems. In: Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Studies in Fuzziness and Soft Computing, vol. 56, pp. 49–88. Physica-Verlag, Heidelberg (2000)
Øhrn, A.: Discernibility and Rough Sets in Medicine: Tools and Applications. PhD Thesis, Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway (1999)
RSES 2.2 User’s Guide. Warsaw University. http://logic.mimuw.edu.pl/~rses/
Predki, B., Wilk, S.: Rough Set Based Data Exploration Using ROSE System. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS, vol. 1609, pp. 172–180. Springer, Heidelberg (1999)
Chouchoulas, A., Shen, Q.: Rough Set-Aided Keyword Reduction For Text Categorization. Applied Artificial Intelligence 15, 843–873 (2001)
Hu, X., Lin, T., Han, J.: A New Rough Sets Model Based on Database Systems. Fundamenta Informaticae 59(2-3), 135–152 (2004)
Freeman, R.L., et al.: Analyzing the Relation Between Heart Rate, Problem Behavior, and Environmental Events Using Data Mining System LERS. In: 14th IEEE Symposium on Computer-Based Medical Systems (CBMS’01), IEEE Computer Society Press, Los Alamitos (2001)
Ivo, D., Gunther, G.: The Rough Set Engine GROBIAN. In: Proc. of the 15th IMACS World Congress, vol. 4, Berlin (August 1997)
Hu, T., et al.: DBROUGH: A Rough Set Based Knowledge Discovery System. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 386–395. Springer, Heidelberg (1994)
Hilderman, R., Hamilton, H.: Knowledge discovery and interestingness measures: A survey. Technical Report 99-04, Department of Computer Science, University of Regina (October 1999)
Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Processings of SIGKDD, pp. 32–41 (2002)
Bruha, I.: Quality of Decision Rules: Definitions and Classification Schemes for Multiple Rules. In: Nakhaeizadeh, G., Taylor, C.C. (eds.) Machine Learning and Statistics, The Interface, pp. 107–131. ohn Wiley & Sons, Chichester (1997)
An, A., Cercone, N.: ELEM2: A Learning System for More Accurate Classifications. In: Proceedings of Canadian Conference on AI, pp. 426–441 (1998)
An, A., Cercone, N.: Rule Quality Measures for Rule Induction Systems: Description and Evaluation. Computational Intelligence 17(3), 409–424 (2001)
Li, J., Cercone, N.: Assigning Missing Attribute Values Based on Rough Sets Theory. In: Proceedings of IEEE Granular Computing, Atlanta, USA, IEEE Computer Society Press, Los Alamitos (2006)
Li, J., Cercone, N.: Predicting Missing Attribute Values based on Frequent Itemset and RSFit. Technical Report, CS-2006-13, School of Computer Science, University of Waterloo (2006)
Li, J., Cercone, N.: Empirical Analysis on the Geriatric Care Data Set Using Rough Sets Theory. Technical Report, CS-2005-05, School of Computer Science, University of Waterloo (2005)
Borgelt, C.: Efficient Implementations of Apriori and Eclat. Proceedings of the FIMI’03 Workshop on Frequent Itemset Mining Implementations. In: CEUR Workshop Proceedings (2003), http://CEUR-WS.org/Vol-90/borgelt.pdf
Bazan, J., et al.: Rough Set Approach to the Survival Analysis. In: Alpigini, J.J., et al. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 522–529. Springer, Heidelberg (2002)
Bazan, J., et al.: Searching for the Complex Decision Reducts: The Case Study of the Survival Analysis. In: Zhong, N., et al. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 160–168. Springer, Heidelberg (2003)
Kusiak, A., Dixon, B., Shah, S.: Predicting Survival Time for kidney Dialysis Patients: A Data Mining Approach. Computers in Biology and Medicine 35, 311–327 (2005)
Pattaraintakorn, P., Cercone, N., Naruedomkul, K.: Selecting Attributes for Soft-Computing Analysis in Hybrid Intelligent Systems. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 698–708. Springer, Heidelberg (2005)
Pattaraintakorn, P., Cercone, N., Naruedomkul, K.: Rule Analysis with Rough Sets Theory. In: The IEEE International Conference on Granular Computing, Atlanta, USA, IEEE, Los Alamitos (2006)
Elisa, L.T., John, W.W.: Statistical methods for survival data analysis, 3rd edn. John Wiley and Sons, New York (2003)
Klein, J.P., Moeschberger, M.L.: Survival analysis: techniques for censored and truncated data, 2nd edn. Springer, Berlin (2003)
Newman, D.J., et al.: UCI Repository of machine learning databases. University of California, Irvine, Department of Information and Computer Seiences (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
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Li, J., Pattaraintakorn, P., Cercone, N. (2007). Rule Evaluations, Attributes, and Rough Sets: Extension and a Case Study. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J., Orłowska, E., Polkowski, L. (eds) Transactions on Rough Sets VI. Lecture Notes in Computer Science, vol 4374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71200-8_9
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DOI: https://doi.org/10.1007/978-3-540-71200-8_9
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