A rough set framework for learning in a directed acyclic graph

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

Volumn 2475, Issue , 2002, Pages 144-155

  Midelfart, Herman ^a   Komorowski, Jan ^a  

^a NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

DIRECTED GRAPHS; GENES; LEARNING ALGORITHMS; LEARNING SYSTEMS;

DECISION CLASS; DIRECTED ACYCLIC GRAPH (DAG); EXPRESSION PROFILE; GENE FUNCTION; LEARNING METHODS; LEARNING PROBLEM;

ROUGH SET THEORY;

EID: 84942868966     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45813-1_18     Document Type: Conference Paper

References (13)

1. M. P. S. Brown, W. N. Grundy, D. Lin, N. Cristianini, C. W. Sugnet, T. S. Furey, M. Ares Jr., and D. Haussler. Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS, 97(1):262–267, 2000.
2. The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nature Genetics, 25(1):25–29, 2000.
3. M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein. Cluster analysis and display of genome-wide expression patterns. PNAS, 95(25):14863–14868, 1998.
4. S. Greco, B. Matarazzo, and R. Słowiński. A new rough set approach to multicriteria and multiattribute classification. In Proc. of the 1st Intl. Conf. on Rough Sets and Current Trends in Computing, LNAI 1424, pp. 60–67. Springer-Verlag, 1998.
5. J. W. Grzymala-Busse. LERS–A system for learning from examples based on rough sets. In Intelligent decision support: Handbookof Applications and Advances of Rough Sets Theory, pp. 3–18. Kluwer Academic Publishers, 1992.
6. T. R. Hvidsten, J. Komorowski, A. K. Sandvik, and A. Lægreid. Predicting gene function from gene expressions and ontologies. In Proceedings of the Pacific Symposium on Biocomputing 6 (PSB-2001), pp. 299–310. World Scientific Press, 2001.
7. J. Komorowski, Z. Pawlak, L. Polkowski, and A. Skowron. A rough set perspective on data and knowledge. In Rough Fuzzy Hybridization, pp. 107–121. Springer-Verlag, 1999.
8. J. Małuszyński and A. Vitoria. Towards rough datalog: embedding rough sets in prolog. To appear in Rough-Neuro Computing, AI series. Springer-Verlag, 2002.
9. H. Midelfart, A. Lægreid, and J. Komorowski. Classification of gene expression data in an ontology. In Proceedings of Second Symposium on Medical Data Analysis (ISMDA-2001), LNCS 2199, pp. 186–194. Springer-Verlag, 2001.
10. Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, 1991.
11. M. Schena, D. Shalon, R. Davis, and P. Brown. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270:467–470, 1995.
12. R. Sokal and C. Mitchener. A statistical method for evaluation systematic relationships. University of Kansas Science Bulletin, 38:1409–1438, 1958.
13. W. Ziarko. Variable precision rough set model. Journal of Computer and System Sciences, 46:39–59, 1993.