Stochastic local search for the Feature Set problem, with applications to microarray data

Applied Mathematics and Computation

Volumn 183, Issue 2, 2006, Pages 1148-1164

  Albrecht, Andreas A ^a  

^a UNIVERSITY OF HERTFORDSHIRE   (United Kingdom)

Author keywords

Feature Set problem; Gene expression analysis; Microarrays; Parameterized complexity; Simulated annealing; Stochastic local search

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA REDUCTION; ERROR ANALYSIS; PROBLEM SOLVING; SIMULATED ANNEALING;

FEATURE SET PROBLEM; GENE-EXPRESSION ANALYSIS; MICROARRAYS; PARAMETERIZED COMPLEXITY; STOCHASTIC LOCAL SEARCH;

RANDOM PROCESSES;

EID: 33845802629     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2006.05.128     Document Type: Article

References (25)

1. Albrecht A. A problem-specific convergence bound for simulated annealing-based local search. Proceedings of the International Conference on Computational Sciences and its Applications. Lecture Notes in Computer Science vol. 3045 (2004), Springer-Verlag 405-414
2. Albrecht A., Vinterbo S.A., and Ohno-Machado L. An Epicurean learning approach to gene-expression data classification. Artif. Intell. Med. 28 (2003) 75-87
3. Blum A.L., and Langley P. Selection of relevant features and examples in machine learning. Artif. Intell. 97 (1997) 245-271
4. Busygin S., Prokopyev O.A., and Pardalos P.M. Feature selection for consistent biclustering via fractional 0-1 programming. J. Comb. Optim. 10 (2005) 7-21
5. Catoni O. Rough large deviation estimates for simulated annealing: applications to exponential schedules. Ann. Probab. 20 (1992) 1109-1146
6. Černy V. A thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 45 (1985) 41-51
7. Chu W., Ghahramani Z., Falciani F., and Wild D.L. Biomarker discovery in microarray gene expression data with Gaussian processes. Bioinformatics 21 (2005) 3385-3393
8. Cotta C., and Moscato P. The k-Feature Set problem is W[2]-complete. J. Comput. Syst. Sci. 67 (2003) 686-690
9. Davies S., and Russell S. NP-completeness of searches for the smallest possible feature set. Proceedings of the AAAI Symposium on Relevance (1994), AAAI Press 41-43
10. Downey R., and Fellows M. Parameterized Complexity (1998), Springer-Verlag, Berlin
11. Furey T.S., Cristianini N., Duffy N., Bednarski D.W., Schummer M., and Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16 (2000) 906-914
12. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D., and Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-537
13. Guyon I., Weston J., Barnhill St., and Vapnik V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46 (2002) 389-422
14. Hajek B. Cooling schedules for optimal annealing. Math. Oper. Res. 13 (1988) 311-329
15. Kirkpatrick S., Gelatt Jr. C.D., and Vecchi M.P. Optimization by simulated annealing. Science 220 (1983) 671-680
16. Kuo W.P., Kim E.-Y., Trimarchi J., Jenssen T.-K., Vinterbo S.A., and Ohno-Machado L. A primer on gene expression and microarrays for machine learning researchers. J. Biomed. Inform. 37 (2004) 293-303
17. Lappas G., Frank R.J., and Albrecht A.A. A computational study on circuit size vs. circuit depth. Int. J. Artif. Intell. Tools 15 (2006) 143-162
18. Li F., and Yang Y. Analysis of recursive gene selection approaches from microarray data. Bioinformatics 21 (2005) 3741-3747
19. Ohno-Machado L., and Kuo W.P. Stochastic algorithms for gene expression analysis. In: Albrecht A., and Steinhöfel K. (Eds). Proceedings of the Second International Symposium on Stochastic Algorithms: Foundations and Applications. Lecture Notes in Computer Science vol. 2877 (2003), Springer-Verlag 39-49
20. Robbins H. A remark on Stirling's formula. Am. Math. Monthly 62 (1955) 26-29
21. Schmitt L.M. Theory of genetic algorithms. Theor. Comput. Sci. 259 (2001) 1-61
22. Shen J., Chang S.I., Lee E.S., Deng Y., and Brown S.J. Determination of cluster number in clustering microarray data. Appl. Math. Comput. 169 (2005) 1172-1185
23. Silva P.J.S., Hashimoto R.F., Kim S., Barrera J., Brandão L.O., Suh E., and Dougherty E.R. Feature selection algorithms to find strong genes. Pattern Recog. Lett. 26 (2005) 1444-1453
24. Wolfinger M.T., Svrcek-Seiler W.A., Flamm Ch., Hofacker I.L., and Stadler P.F. Exact folding dynamics of RNA secondary structures. J. Phys. A-Math. Gen. 37 (2004) 4731-4741
25. Vapnik V. Statistical Learning Theory (1998), Wiley & Sons, New York