Minimal cost attribute reduction through backtracking

Communications in Computer and Information Science

Volumn 258 CCIS, Issue , 2011, Pages 100-107

  Min, Fan ^a   Zhu, William ^a  

^a MINNAN NORMAL UNIVERSITY   (China)

Author keywords

attribute reduction; backtrack algorithm; Cost sensitive learning; misclassification cost; test cost

Indexed keywords

ATTRIBUTE REDUCTION; COST-SENSITIVE LEARNING; DATA SETS; DECISION SYSTEMS; GLOBAL PERSPECTIVE; LOCAL PERSPECTIVE; MINIMAL COST; MISCLASSIFICATION COSTS; PRUNING TECHNIQUES; TEST COST; TEST SETS; TOTAL COSTS;

ALGORITHMS; DECISION TREES; TESTING;

COSTS;

EID: 83755161462     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-27157-1_11     Document Type: Conference Paper

References (21)

1. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/mlrepository.html
2. Dash, M., Liu, H.: Consistency-based search in feature selection. Artificial Intelligence 151, 155-176 (2003)
3. Elkan, C.: The foundations of cost-sensitive learning. In: IJCAI, pp. 973-978 (2001)
4. Hunt, E.B., Marin, J., Stone, P.J. (eds.): Experiments in induction. Academic Press, New York (1966)
5. Ling, C.X., Sheng, V.S., Yang, Q.: Test strategies for cost-sensitive decision trees. IEEE Transactions on Knowledge and Data Engineering 18(8), 1055-1067 (2006)
6. Min, F., He, H., Qian, Y., Zhu, W.: Test-cost-sensitive attribute reduction. Information Sciences 181, 4928-4942 (2011)
7. Min, F., Liu, Q.: A hierarchical model for test-cost-sensitive decision systems. Information Sciences 179, 2442-2452 (2009)
8. Min, F., Zhu, W.: Attribute reduction with test cost constraint. Journal of Electronic Science and Technology of China 9(2), 97-102 (2011)
9. Min, F., Zhu, W., Zhao, H., Pan, G.: Coser: Cost-senstive rough sets (2011), http://grc.fjzs.edu.cn/~fmin/coser/
10. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341-356 (1982)
11. Quinlan, J.R. (ed.): C4.5 Programs for Machine Learning. Morgan kaufmann Publisher, San Francisco (1993)
12. Skowron, A., Rauszer, C.: The discernibility matrics and functions in information systems. In: Intelligent Decision Support (1992)
13. Ślȩzak, D.: Approximate entropy reducts. Fundamenta Informaticae 53(3-4), 365-390 (2002)
14. Ślȩzak, D., Widz, S.: Is It Important Which Rough-Set-Based Classifier Extraction and Voting Criteria Are Applied Together? In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS, vol. 6086, pp. 187-196. Springer, Heidelberg (2010)
15. Susmaga, R.: Computation of Minimal Cost Reducts. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS, vol. 1609, pp. 448-456. Springer, Heidelberg (1999)
16. Turney, P.D.: Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research 2, 369-409 (1995)
17. Wang, G.: Attribute Core of Decision Table. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 213-217. Springer, Heidelberg (2002)
18. Yao, Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Information Sciences 178(17), 3356-3373 (2008)
19. Zhou, Z., Liu, X.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering 18(1), 63-77 (2006)
20. Zhu, W.: Generalized rough sets based on relations. Information Sciences 177(22), 4997-5011 (2007)
21. Zhu,W.,Wang, F.: Reduction and axiomization of covering generalized rough sets. Information Sciences 152(1), 217-230 (2003)