Is it important which rough-set-based classifier extraction and voting criteria are applied together?

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

Volumn 6086 LNAI, Issue , 2010, Pages 187-196

  Ślȩzak, Dominik ^a,b   Widz, Sebastian ^c,d  

^a UNIVERSITY OF WARSAW   (Poland)

^b INFOBRIGHT INC   (Poland)

^c XPLUS SA   (Poland)

^d POLISH JAPANESE INSTITUTE OF INFORMATION TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ATTRIBUTE REDUCTION; CLASSIFICATION RESULTS; DATA ANALYSIS; EXPERIMENTAL VERIFICATION; HIGH QUALITY; LOW QUALITIES; MATHEMATICAL PRINCIPLE; PROBABILISTIC EXTENSION;

DATA REDUCTION;

ROUGH SET THEORY;

EID: 79956271368     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13529-3_21     Document Type: Conference Paper

References (16)

1. Bazan, J.G., Nguyen, H.S.,Nguyen, S.H., Synak, P.,Wróblewski, J.:Rough SetAlgorithms in Classification Problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) New Developments in Knowledge Discovery in Information Systems, pp. 49-88. Physica Verlag, Heidelberg (2000)
2. Błaszczyński, J., Greco, S., Słowiński, R., Szela̧g, M.: Monotonic Variable Consistency Rough Set Approaches. Int. J. Approx. Reasoning 50(7), 979-999 (2009)
3. Grzymała-Busse, J.W.: Rule Induction, Missing Attribute Values and Discretization. Encyclopedia of Complexity and Systems Science, 7797-7804 (2009)
4. Jensen, R., Shen, Q.: Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. Wiley, IEEE (2008)
5. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, Chichester (2004)
6. Pawlak, Z., Skowron, A.: Rudiments of Rough Sets. Inf. Sci. 177(1), 3-27 (2007) (Pubitemid 44556698)
7. Ślȩzak, D.: Normalized Decision Functions and Measures for Inconsistent Decision Tables Analysis. Fundam. Inform. 44(3), 291-319 (2000)
8. Ślȩzak, D.: Rough Sets and Functional Dependencies in Data: Foundations of Association Reducts. T. Computational Science 5, 182-205 (2009)
9. Ślȩzak, D., Sakai, H.: Automatic Extraction of Decision Rules from Non-deterministic Data Systems: Theoretical Foundations and SQL-Based Implementation. In: Proc. of DTA 2009. CCIS, vol. 64, pp. 151-162. Springer, Heidelberg (2009)
10. Ślȩzak, D., Wróblewski, J.: Roughfication of Numeric Decision Tables: The Case Study of GeneExpressionData. In:Yao, J.,Lingras,P.,Wu,W.-Z.,Szczuka,M.S.,Cercone,N.J., Ślȩzak, D. (eds.) RSKT 2007. LNCS (LNAI), vol. 4481, pp. 316-323. Springer, Heidelberg (2007) (Pubitemid 350284253)
11. Ślȩzak, D., Ziarko, W.: The Investigation of the Bayesian Rough Set Model. Int. J. Approx. Reasoning 40(1-2), 81-91 (2005) (Pubitemid 40710220)
12. Szczuka, M.S.: Refining classifiers with neural networks. Int. J. Intell. Syst. 16(1), 39-55 (2001) (Pubitemid 32089956)
13. Widz, S., Ślȩzak, D.: Approximation Degrees in Decision Reduct-Based MRI Segmentation. In: Proc. of FBIT 2007, pp. 431-436. IEEE CS, Los Alamitos (2007)
14. Wróblewski, J.: Adaptive Aspects of Combining Approximation Spaces. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing, pp. 139-156. Springer, Heidelberg (2004)
15. Yao, Y.Y., Zhao, Y., Wang, J.: On Reduct Construction Algorithms. T. Computational Science 2, 100-117 (2008)
16. Ziarko,W.: Probabilistic Approach to Rough Sets. Int. J. Approx. Reasoning 49(2), 272-284 (2008)