Multi-agent based multi-knowledge acquisition method for rough set

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5009 LNAI, Issue , 2008, Pages 140-147

  Liu, Yang ^a,b   Bai, Guohua ^b   Feng, Boqin ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b BLEKINGE INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Attribute reduction; Classification accuracy; Knowledge acquisition; Multi agent technology

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; ALGORITHMS; BOOLEAN FUNCTIONS; COMPUTER NETWORKS; DATA ACQUISITION; DECISION SUPPORT SYSTEMS; EVOLUTIONARY ALGORITHMS; FUZZY SETS; INFORMATION MANAGEMENT; INFORMATION SYSTEMS; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE MANAGEMENT; MANAGEMENT INFORMATION SYSTEMS; MERGERS AND ACQUISITIONS; MULTI AGENT SYSTEMS; NUMBER THEORY; NUMERICAL METHODS; POWDERS; ROUGH SET THEORY; SCHEDULING ALGORITHMS; SEARCH ENGINES; SET THEORY; TECHNOLOGY; TECHNOLOGY TRANSFER; TRANSIENTS;

(OTDR) TECHNOLOGY; AGENT BASED; CLASSIFICATION ACCURACIES; DATA-SETS; DISTRIBUTED DATA MINING (DDM); HEIDELBERG (CO); INTERNATIONAL CONFERENCES; LARGE-SCALE DATASETS; MULTI AGENTS; MULTI-AGENT TECHNOLOGIES; NOVE L APPROACH; ROUGH SETS; SPRINGER (CO); STATE-OF-THE-ART METHODS; SUB-SYSTEMS;

KNOWLEDGE ACQUISITION;

EID: 44649109515     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-79721-0_23     Document Type: Conference Paper

References (15)

1. Pawlak, Z.: Rough sets. International J. Comp. Inform. Science 11, 341-356 (1982)
2. Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht, Boston, London (1991)
3. Wang, G.Y., Liu, F.: The inconsistency in rough set based rule generation. In: Rough Sets and Current Trends in Computing, pp. 370-377 (2000)
4. Yasdi, R.: Combining rough sets learning-method and neural learning-method to deal with uncertain and imprecise information. Neuro-Computing 7(1), 61-84 (1995)
5. Wu, Q.: Bell, David: Multi-knowledge extraction and application. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 274-278. Springer, Heidelberg (2003)
6. Hang, X.S., Dai, H.: An optimal strategy for extracting probabilistic rules by combining rough sets and genetic algorithm. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds.) DS 2003. LNCS (LNAI), vol. 2843, pp. 153-165. Springer, Heidelberg (2003)
7. Klusch, M., Lodi, S., Moro, G.: Issues of agent-based distributed data mining. In: International Conference on Autonomous Agents, pp. 1034-1035 (2003)
8. Wooldridge, M.J., Jennings, N.R.: Intelligent agent: theory and practice. Knowledge Engineering Review 10(2), 115-152 (1995)
9. Pynadath, D.V., Tambe, M.: The communicative multiagent team decision problem: Analyzing teamwork theories and models. Journal of Artificial Intelligence Research, 389-423 (2002)
10. Yaskawa, S., Sakata, A.: The application of intelligent agent technology to simulation. Mathematical and Computer Modelling 37(9), 1083-1092 (2003)
11. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences, 3-27 (2007)
12. Grzymala-Busse, J.W.: MLEM2 - Discretization during rule induction. Intelligent Information Systems, 499-508 (2003)
13. UCI machine learning repository, http://archive.ics.uci.edu/ml/
14. Tay, F.E., Shen, L.: A modified Chi2 algorithm for discretization. IEEE Transactions on Knowledge and Data Engineering 14(3), 666-670 (2002)
15. Aggarwal, C.C., Parthasarathy, S.: Mining massively incomplete data sets by conceptual reconstruction. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 227-232 (2001)