IP algorithms in compact rough classification modeling

Intelligent Data Analysis

Volumn 5, Issue 5, 2001, Pages 419-429

  Bakar, Azuraliza Abu ^a   Sulaiman, Md Nasir ^a   Othman, Mohamed ^a   Selamat, Mohd Hasan ^a  

^a UNIVERSITY PUTRA MALAYSIA   (Malaysia)

Author keywords

decision system (DS); integer programming (IP); reduct; rough set

Indexed keywords

DECISION THEORY; INTEGER PROGRAMMING;

0-1 INTEGER PROGRAMMING MODELS; BRANCH AND BOUND SEARCH; CLASSIFICATION ACCURACY; DECISION SYSTEMS; DISCERNIBILITY; REDUCT; REDUCT IN ROUGH SET THEORY; ROUGH CLASSIFICATION;

ROUGH SET THEORY;

EID: 33645989000     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2001-5505     Document Type: Article

References (14)

1. R.J. Bayardo and R. Agrawal, Mining The Most Interesting Rules, Proc. of the 5th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, 1999, pp. 145-154.
2. J.G. Bazan, A Comparison of Dynamic and Non-dynamic Rough Set Methods for Extracting Laws from Decision Tables, in: Rough Sets in Knowledge Discovery: Methodology and Applications, (Chapter 17), Physica Verlag, 1998, pp. 321-265.
3. M.L. Ginsberg, Dynamic Backtracking, Journal of Artificial Intelligence Research 1 (1993), 25-46.
4. F.S. Hillier and G.J. Lieberman, Introduction to Operations Research, McGraw Hill, 1989.
5. R.C. Holte, Very Simple Classification Rules Perform Well on Most Commonly Used Datasets, Machine Learning 11 (1993), 63-92.
6. T. Mollestad and J. Komorowski, A Rough Set Framework of Propositional Default Rules Data Mining, in: Fuzzy Sets, Rough Sets and Decision Making Processes, S.K. Pal and A. Skowron, eds, Springer-Verlag Singapore Pte Ltd, 1998.
7. Z. Pawlak, Rough Set: Theoretical Aspect of Reasoning about Data, Kluwer Publications, 1991.
8. J.P.M. Silva, On Computing Minimum Size Prime Implicant, International Workshop on Logic Synthesis, May 1997, URL: http://vinci.inesc.pt/jpms/recent- publications.html/Heidelberg, 1998, pp. 109-126.
9. J.P.M. Silva and K.A. Sakallah, GRASP: A Search Algorithm for Propositional Satisfiability, IEEE Transactions on Computers 48(5) (1999), 506-521.
10. P.M. Murphy, UCI Repositories of Machine Learning and Domain Theories, URL:/http://www.ics.uci.edu/mlearn/MLRepository.html.
11. A. Ohrm, ROSETTA technical Reference Manual. Knowlegde System Group, Norwegian University of Science and Technology, Trondheim, Norway, 1998.
12. G. Piatestsky-Shapiro, Discovery, Analysis and Presentation of Strong Rules, Chapter 13 of Knowledge Discovery in Databases, AAAI/MIT Press, 1991.
13. J. Stepaniuk, Approximation Spaces, Reducts and Representative, in: Rough Sets in Knowledge Discovery 2 Applications, Case Studies and Software Systems, L. Polkowski and A. Skowron, eds, Physica-Verlag, Heidelberg, 1998, pp. 109-126.
14. J. Wroblewski, Finding Minimal Reducts Using Genetic Algorithm, Proc. of the 2nd Annual Join Conference on Information Science, Sep. 28-Oct. 1, 1995, pp. 186-189.