SPECIAL ISSUE: Active Media Technology for Intelligent IT; Edited by Y. Li and N. ZhongNo Access

A MULTISTAGE RULE INDUCTION ALGORITHM IN CLASSIFICATION

College of Computer Science, Jilin University, Changchun 130012, P.R. China

Key Laboratory of Symbolic Computation and Knowledge, Engineering of Ministry of Education, Changchun 130012, P.R. China

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HUAWEN LIU

College of Computer Science, Jilin University, Changchun 130012, P.R. China

Key Laboratory of Symbolic Computation and Knowledge, Engineering of Ministry of Education, Changchun 130012, P.R. China

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CHANGSONG QI

Department of Computer Science, Tonghua Normal College, Tonghua 134002, P.R. China

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https://doi.org/10.1142/S0218001407005624Cited by:2 (Source: Crossref)

Abstract

The purpose of this paper is to start a conceptual investigation of approximation rule based on VPRS as a result of the certainty degree of rules in complete information system that cannot exactly express the uncertainty of those in incomplete information system, and then an efficient approximation rule induction algorithm under the rough set framework is presented. Instead of focusing on the minimal rule set, this algorithm hierarchically extracts rules in multistages from data sets to suit changing environments in learning and classification. In addition, a heuristic strategy is employed in the algorithm to improve its performance and reduce the time consumed in inducing. Experiments are carried out, and the results show that the proposed algorithm is effective in inducing rules which can enhance their adaptive capacities.

Keywords:

References

J. Anet al., A rule generation algorithm based on granular computing, Proc. 2005 IEEE Int. Conf. Granular Computing1 (2005) pp. 102–107, DOI: 10.1109/GRC.2005.1547244. Google Scholar
C. L. Blake and C. J. Merz, UCI repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA (1998) http://www.ics.uci.edu/~learn/MLRepository.html . Google Scholar
J. H. Friedman, R. Kohavi and Y. Yun, Lazy decision trees, Proc. AAAI96 and IAAI961, eds. H. Shrobe and T. Senator (AAAI Press/The MIT Press, 1996) pp. 717–724. Google Scholar
J. W. Grzymala-Busse, Transactions on Rough Sets I, eds. J. F. Peters and A. Skowron (Springer, 2004) pp. 78–95. Crossref, Google Scholar
J. W. Grzymala-Busse and M. Hu, Rough Sets and Current Trends in Computing 2000, eds. W. Ziarko and Y. Yao (Springer, 2001) pp. 378–385. Crossref, Google Scholar
J. W. Guan and D. A. Bell, Artif. Intell. 105, 77 (1998), DOI: 10.1016/S0004-3702(98)00090-3. Crossref, Web of Science, Google Scholar
T. P. Hong, L. H. Tseng and S. L. Wang, Expert Systems Appl. 22, 285 (2002), DOI: 10.1016/S0957-4174(02)00016-7. Crossref, Web of Science, Google Scholar
Q. H. Hu, D. R. Yu and M. Y. Wang, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, eds. D. Ślȩzaket al. (2005) pp. 147–156. Crossref, Google Scholar
J. Kittleret al., IEEE Trans. Patt. Anal. Mach. Intell. 20, 226 (1998), DOI: 10.1109/34.667881. Crossref, Web of Science, Google Scholar
M. Kryszkiewicz, Inform. Sci. 113, 271 (1999), DOI: 10.1016/S0020-0255(98)10065-8. Crossref, Web of Science, Google Scholar
R. Latkowski and M. Mikolajczyk, Transactions on Rough Sets I, eds. J. F. Peters and A. Skowron (Springer, 2004) pp. 299–320. Crossref, Google Scholar
T. Y. Lin and P. Yin, Rough Sets and Current Trends in Computing 2004, eds. S. Tsumotoet al. (Springer, Berlin, 2004) pp. 465–470. Crossref, Google Scholar
H. Liuet al., Data Min. Knowl. Discov. 6, 393 (2002), DOI: 10.1023/A:1016304305535. Crossref, Web of Science, Google Scholar
Z. Pawlak , Rough Sets-Theoretical Aspects of Reasoning about Data ( Kluwer Academic Publishers , Boston, London , 1991 ) . Google Scholar
L. Polkowski and A. Skowron, Int. J. Approx. Reas. 15, 333 (1996), DOI: 10.1016/S0888-613X(96)00072-2. Crossref, Web of Science, Google Scholar
J. A. Pomykala, Rough Sets and Current Trends in Computing 2002, eds. J. J. Alpiginiet al. (2002) pp. 175–182. Crossref, Google Scholar
J. R. Quinlan , C4.5: Programs for Machine Learning ( Morgan Kaufmann , San Mateo, CA , 1993 ) . Google Scholar
RSES: rough set exploration system, Institute of Mathematics, Warsaw University, Poland, http://logic.mimuw.edu.pl/~ses . Google Scholar
A. Skowron and C. Rauszer, Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, ed. R. Slowinski (Kluwer Academic Publishers, Dordrecht, 1992) pp. 331–362. Crossref, Google Scholar
A. Skowronet al., Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, eds. D. Ślȩzaket al. (2005) pp. 119–127. Crossref, Google Scholar
J. Stefanouski and A. Tsoukias, On the extension of rough sets under incomplete information, Proc. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing 1999 (Springer, Berlin, 1999) pp. 73–81. Google Scholar
Z. Suraj, K. Pancerz and G. Owsiany, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, eds. D. Ślȩzaket al. (2005) pp. 224–233. Crossref, Google Scholar
Y. C. Tsai, C. H. Cheng and J. R. Chang, Exp. Syst. Appl. 31, 436 (2006), DOI: 10.1016/j.eswa.2005.09.038. Crossref, Web of Science, Google Scholar
S. Tsumoto, Expert Syst. Appl. 24, 189 (2003), DOI: 10.1016/S0957-4174(02)00142-2. Crossref, Web of Science, Google Scholar
F. H. Wang, Exp. Syst. Appl. 29, 49 (2005), DOI: 10.1016/j.eswa.2005.01.005. Crossref, Web of Science, Google Scholar
H. Wang, I. Düntsch and G. Gediga, Int. J. Approx. Reas. 23, 111 (2000), DOI: 10.1016/S0888-613X(99)00040-7. Crossref, Web of Science, Google Scholar
S. M. Weiss and N. Indurkhya, Lightweight rule induction, Proc. Int. Conf. Machine Learning ICML'2000 (2002) pp. 1135–1142. Google Scholar
Q. X. Wu and D. Bell, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, eds. G. Wanget al. (Springer, Berlin, 2003) pp. 274–278. Crossref, Google Scholar
Y. Y. Yao, On modeling data mining with granular computing, Proc. COMPSAC 2001 (2001) pp. 638–643, DOI: 10.1109/CMPSAC.2001.960680. Google Scholar
N. Zhong, J. Dong and S. Ohsuga, Neurocomputing 36, 171 (2001), DOI: 10.1016/S0925-2312(00)00341-6. Crossref, Web of Science, Google Scholar
W. Ziarko, J. Comput. Syst. Sci. 46, 39 (1993), DOI: 10.1016/0022-0000(93)90048-2. Crossref, Web of Science, Google Scholar