Abstract
The rough-set theory has been widely used in dealing with data classification problems. Most of the previous studies on rough sets focused on deriving certain rules and possible rules on the single concept level. Data with hierarchical attribute values are, however, commonly seen in real-world applications. This paper thus attempts to propose a new learning algorithm based on rough sets to find cross-level certain and possible rules from training data with hierarchical attribute values. It is more complex than learning rules from training examples with single-level values, but may derive more general knowledge from data.
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References
Buchanan BG, Shortliffe EH (1984) Rule-Based Expert System: The MYCIN
Experiments of the Standford Heuristic Programming Projects. Massachusetts: Addison-Wesley
Chmielewski MR, Grzymala-Busse JW, Pererson NW, Than S (1993) The rule induction system LERS — A version for personal computers. Foundation of Computation Decision Science Vol 18, No 3, pp 181–212
Grzymala-Busse JW (1988) Knowledge acquisition under uncertainty: A rough set approach. Journal of Intelligent Robotic Systems. Vol 1, pp 3–16
Hirano S, Sun X, Tsumoto S (2002) Dealing with multiple types of expert knowledge in medical image segmentation: A rough sets style approach. The 2002 IEEE International Conference on Fuzzy system. Vol 2, pp 884–889
Hong T.P, Wang TT, Wang SL (2000) Knowledge acquisition from quantitative data using the rough-set theory. Intelligent Data Analysis. Vol 4, pp 289–304
Hong TP, Wang TT, Chien BC (2001) Mining approximate coverage rules. International Journal of Fuzzy Systems. Vol 3, No 2, pp 409–414
Lambert-Torres G, Alves DA, Silva AP, Quintana VH, Borges DA, Silva LE (1996) Knowledge-base reduction based on rough set techniques. The Canadian Conference on Electrical and Computer Engineering. pp 278–281
Lee CH, Swo SH, Choi SC (2001) Rule discovery using hierarchical classification structure with rough sets. The 9th IFSA World Congress and the 20th NAFIPS International Conference. Vol 1, pp 447–452
Lingras PJ, Yao YY (1998) Data mining using extensions of the rough set model. Journal of the American Society for Information Science. Vol 49, No 5, pp 415–422
Michalski RS, Carbonell JG, Mitchell TM (1983) Machine Learning: An Artificial Intelligence Approach. Vol 1, California: Kaufmann Publishers
Michalski RS, Carbonell JG, Mitchell TM (1984) Machine Learning: An Artificial Intelligence Approach. Vol 2.
Pawlak Z (1982) Rough set, International Journal of Computer and Information Sciences. Vol 11, No 5, pp 341–356
Pawlak Z (1991) Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers
Z Pawlak (1996) Why rough sets? The Fifth IEEE International Conference on Fuzzy Systems. Vol 2, pp 738 —743
Tsumoto S (1998) Extraction of experts decision rules from clinical databases using rough set model. Intelligent Data Analysis. Vol 2, pp 215–227
Tsumto S (1998) Knowledge discovery in medical databases based on rough sets and attribute-oriented generalization. The 1998 IEEE International Conference on Fuzzy Systems. Vol 2, pp 1296–1301
Yao YY (1999) Stratified rough sets and granular computing. The 18th International Conference of the North American. pp 800–804
Zhong N, Dong JZ, Ohsuga S, Lin TY (1998) An incremental probabilistic rough set approach to rule discovery. The IEEE International Conference on Fuzzy Systems. Vol 2, pp 933 —938
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Hong, TP., Lin, CE., Lin, JH., Wang, SL. (2003). Learning from Hierarchical Attribute Values by Rough Sets. In: Abraham, A., Franke, K., Köppen, M. (eds) Intelligent Systems Design and Applications. Advances in Soft Computing, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44999-7_53
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DOI: https://doi.org/10.1007/978-3-540-44999-7_53
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