A weighted random forests approach to improve predictive performance

Statistical Analysis and Data Mining

Volumn 6, Issue 6, 2013, Pages 496-505

  Winham, Stacey J ^a   Freimuth, Robert R ^a   Biernacka, Joanna M ^a  

^a MAYO CLINIC   (United States)

Author keywords

Gene gene interactions; Genetic data; Genome wide association; High dimensional data; Random forests; Weighting

Indexed keywords

ASSOCIATION REACTIONS; CLUSTERING ALGORITHMS; DECISION TREES; FILTRATION; FORESTRY; GENES;

COMPLEX DISEASE; EFFECT SIZE; GENE-GENE INTERACTION; GENETIC DATA; GENETIC VARIANTS; GENOME-WIDE ASSOCIATION; HIGH DIMENSIONAL DATA; PREDICTIVE PERFORMANCE; RANDOM FORESTS; WEIGHTING;

DATA MINING;

CHEMICAL REACTIONS; DATA PROCESSING; FORESTRY; GENETIC ENGINEERING; MINING;

EID: 84890157266     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.11196     Document Type: Article

References (20)

1. M. I. McCarthy, G. R. Abecasis, L. R. Cardon, D. B. Goldstein, J. Little, J. P. Ioannidis, and J. N. Hirschhorn, Genome-wide association studies for complex traits: consensus, uncertainty and challenges, Nat Rev Genet 9(5) (2008), 356-369.
2. T. A. Manolio, F. S. Collins, N. J. Cox, D. B. Goldstein, L. A. Hindorff, D. J. Hunter, M. I. McCarthy, E. M. Ramos, L. R. Cardon, A. Chakravarti, J. H. Cho, A. E. Guttmacher, A. Kong, L. Kruglyak, E. Mardis, C. N. Rotimi, M. Slatkin, D. Valle, A. S. Whittemore, M. Boehnke, A. G. Clark, E. E. Eichler, G. Gibson, J. L. Haines, T. F. Mackay, S. A. McCarroll, and P. M. Visscher, Finding the missing heritability of complex diseases, Nature 461(7265) (2009), 747-753.
3. L. Breiman, Random forests, Mach Learn 45 (2001), 5-32.
4. K. L. Lunetta, L. B. Hayward, J. Segal, and P. Van Eerdewegh, Screening large-scale association study data: exploiting interactions using random forests, BMC Genet 5(1) (2004), 32.
5. B. A. Goldstein, A. E. Hubbard, A. Cutler, and L. F. Barcellos, An application of random forests to a genome-wide association dataset: methodological considerations & new findings, BMC Genet 11 (2010), 49.
6. S. J. Winham, C. L. Colby, R. R. Freimuth, X. Wang, M. de Andrade, M. Huebner, and J. M. Biernacka, SNP interaction detection with random forests in high-dimensional genetic data, BMC Bioinformatics 13(1) (2012), 164.
7. C. Chen, A. Liaw, and L. Breiman, Using Random Forest to Learn Imbalanced Data, Department of Univeristy of California at Berkley, Technical Report 666 Statistics, 2004.
8. J. Maudes, J. J. Rodriguez, C. Garcia-Osorio, and N. Garcia-Pedrajas, Random feature weights for decision tree ensemble construction, Inf Fusion 13(1) (2012), 20-30.
9. D. Amaratunga, J. Cabrera, and Y. S. Lee, Enriched random forests, Bioinformatics 24(18) (2008) 2010-2014.
10. H. Kim, H. Moon, and H. Ahn, A weight-adjusted voting algorithm for ensembles of classifiers, J Korean Stat Soc 40(4) (2011), 437-449.
11. M. Robnik-Sikonja, Improving random forests. In Machine Learning: Ecml 2004 Proceedings, Vol. 3201, J. F. Boulicaut, F. Esposito, F. Giannoti, and D. Pedreschi, eds. 2004, 359-370.
12. L. Hong Bo, W. Wei, D. Hong Wei, and D. Jin, Trees weighting random forest method for classifying high-dimensional noisy data. e-Business Engineering (ICEBE), 2010 IEEE 7th International Conference, 2010, 160-163.
13. L. J. Bierut, A. Agrawal, K. K. Bucholz, K. F. Doheny, C. Laurie, E. Pugh, S. Fisher, L. Fox, W. Howells, S. Bertelsen, A. L. Hinrichs, L. Almasy, N. Breslau, R. C. Culverhouse, D. M. Dick, H. J. Edenberg, T. Foroud, R. A. Grucza, D. Hatsukami, V. Hesselbrock, E. O. Johnson, J. Kramer, R. F. Krueger, S. Kuperman, M. Lynskey, K. Mann, R. J. Neuman, M. M. Nothen, J. I. Nurnberger, Jr., B. Porjesz, M. Ridinger, N. L. Saccone, S. F. Saccone, M. A. Schuckit, J. A. Tischfield, J. C. Wang, M. Rietschel, A. M. Goate, and J. P. Rice, A genome-wide association study of alcohol dependence, Proc Natl Acad Sci U S A 107(11), 5082-5087.
14. A. Bureau, J. Dupuis, K. Falls, K. L. Lunetta, B. Hayward, T. P. Keith, and P. Van Eerdewegh, Identifying SNPs predictive of phenotype using random forests, Genet Epidemiol 28(2) (2005), 171-182.
15. R. Culverhouse, B. K. Suarez, J. Lin, and T. Reich, A perspective on epistasis: limits of models displaying no main effect, Am J Hum Genet 70(2) (2002), 461-471.
16. M. D. Mailman, M. Feolo, Y. Jin, M. Kimura, K. Tryka, R. Bagoutdinov, L. Hao, A. Kiang, J. Paschall, L. Phan, N. Popova, S. Pretel, L. Ziyabari, M. Lee, Y. Shao, Z. Y. Wang, K. Sirotkin, M. Ward, M. Kholodov, K. Zbicz, J. Beck, M. Kimelman, S. Shevelev, D. Preuss, E. Yaschenko, A. Graeff, J. Ostell, and S. T. Sherry, The NCBI dbGaP database of genotypes and phenotypes, Nat Genet 39(10) (2007), 1181-1186.
17. V. M. Karpyak, J. R. Geske, C. L. Colby, D. A. Mrazek, and J. M. Biernacka, Genetic variability in the NMDA-dependent AMPA trafficking cascade is associated with alcohol dependence, Addict Biol 17(4) (2012), 798-806.
18. G. Biau, L. Devroye, and G. Lugosi, Consistency of random forests and other averaging classifiers, J Mach Learn Res 9 (2008), 2015-2033.
19. T. Bylander, Estimating generalization error on two-class datasets using out-of-bag estimates, Mach Learn 48(1-3) (2002), 287-297.
20. A. L. Boulesteix and M. Slawski, Stability and aggregation of ranked gene lists, Brief Bioinform 10(5) (2009), 556-568.