Fast randomized algorithm with restart strategy for minimal test cost feature selection

International Journal of Machine Learning and Cybernetics

Volumn 6, Issue 3, 2015, Pages 435-442

  Li, Jingkuan ^a   Zhao, Hong ^a   Zhu, William ^a  

^a MINNAN NORMAL UNIVERSITY   (China)

Author keywords

Feature selection; Restart strategy; Test cost; Time constraint

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COSTS; DATA MINING; LEARNING SYSTEMS;

COST-SENSITIVE; PERFORMANCE OF ALGORITHM; RANDOMIZED ALGORITHMS; RANDOMIZED MECHANISM; RESTART STRATEGY; RUNTIMES; TEST COST; TIME CONSTRAINTS;

FEATURE EXTRACTION;

EID: 84929076957     PISSN: 18688071     EISSN: 1868808X     Source Type: Journal    
DOI: 10.1007/s13042-014-0262-0     Document Type: Article

References (31)

1. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356
2. Zhao H, Min F, Zhu W (2013) Test-cost-sensitive attribute reduction of data with normal distribution measurement errors. Math Probl Eng 2013:1–12
3. Ślęzak D, Ziarko W (2006) The investigation of the bayesian rough set model. Int J Approx Reason 40:81–91
4. Wang XZ, He YL, Wang DD (2014) Non-naive bayesian classifiers for classification problems with continuous attributes. IEEE Trans Cybern 44(1):21–39
5. Nakamatsu K, Abe JM, Suzuki A (2000) Annotated semantics for defeasible deontic reasoning. In: Proceedings of rough sets and current trends in computing. Volume 2005 of LNCS. pp 470–478
6. Nguyen HS (2000) On efficient construction of decision trees from large databases. In: Proceedings of rough sets and current trends in computing. Volume 2005 of LNCS. 354–361
7. Wang XZ, Dong LC, Yan JH (2012) Maximum ambiguity-based sample selection in fuzzy decision tree induction. Knowl Data Eng IEEE Trans 24(8):1491–1505
8. Xie ZX, Xu Y (2014) Sparse group lasso based uncertain feature selection. Int J Mach Learn Cybern 5(2):201–210
9. Rückstieß T, Osendorfer C, van der Smagt P (2013) Minimizing data consumption with sequential online feature selection. Int J Mach Learn Cybern 4:235–243
10. Subrahmanya N, Shin YC (2013) A variational bayesian framework for group feature selection. Int J Mach Learn Cybern 4:609–619
11. Min F, He H, Qian Y, Zhu W (2011) Test-cost-sensitive attribute reduction. Inf Sci 181:4928–4942
12. Pan GY, Min F, Zhu W (2011) A genetic algorithm to the minimal test cost reduct problem. In: Proceedings of IEEE international conference on granular computing pp 539–544
13. Peng HC, Long FH, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. Pattern Anal Mach Intell IEEE Trans 27:1226–1238
14. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learni Res 5:1205–1224
15. Itaia AIA (1986) A fast and simple randomized parallel algorithm for maximal matching. Inf Proc Lett 22(02):77–80
16. Li JK, Min F, Zhu W (2013) Fast randomized algorithm for minimal test cost attribute reduction. Proc ICRITO. 2013:12–17
17. Karlin AR, Manasse MS, McGeoch LA, Owicki S (1994) Competitive randomized algorithms for nonuniform problems. Comput Sci 11(06):542–571
18. Chen G, Xie X, Xu Y, Gu J (2002) Designing restart strategy for randomized algorithms and its application in solving the tsp. Chin J Comput 5:514–519
19. Hoos HH (1998) Stochastic local search-methods, models, applications IOS
20. Hogg T, Williams CP (1994) Expected gains from parallelizing constraint solving for hard problems. In AAAI, CP, pp 331–336
21. Blake CL, Merz CJ (1998) UCI repository of machine learning databases
22. Min F, Zhu W (2012) Attribute reduction of data with error ranges and test costs. Inf Sci 211:48–67
23. He HP, Min F (2011) Accumulated cost based test-cost-sensitive attribute reduction. In: Proceedings of the 13th international conference on rough sets, fuzzy Sets, data mining and granular computing. Volume 6743 of LNAI, 244–247
24. Zhao H, Min F, Zhu W (2013) Cost-sensitive feature selection of numeric data with measurement errors. J Appl Math 2013:1–13
25. Min F, Hu QH, Zhu W (2014) Feature selection with test cost constraint. Int J Approx Reason 55(1–2):167–179
26. Zhang WX, Mi JS, Wu WZ (2003) Knowledge reductions in inconsistent information systems. Chin J Comput Chin Edn 26(1):12–18
27. Jensen R, Shen Q (2004) Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. Knowl Data Eng IEEE Trans 16(12):1457–1471
28. Yao YY, Zhao Y (2008) Attribute reduction in decision-theoretic rough set models. Inf Sci 178:3356–3373
29. Motwani R (1995) Randomized algorithms. Cambridge university press, Cambridge
30. Guntsch M, Middendorf M (2001) Pheromone modification strategies for ant algorithms applied to dynamic tsp. In: Applications of evolutionary computing. Springer, New York 213–222
31. Luby M, Sinclair A, Zuckerman D (1993) Optimal speedup of las vegas algorithms. Inf Proc Lett 47(4):173–180