Variable interaction measures with random forest classifiers

Proceedings - International Symposium on Biomedical Imaging

Volumn , Issue , 2012, Pages 154-157

  Kelly, Cassidy ^a   Okada, Kazunori ^a  

^a SAN FRANCISCO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION PERFORMANCE; DATA SETS; MULTI-CLASS; NONLINEAR INTERACTIONS; RANDOM FOREST CLASSIFIER; RANDOM FORESTS; RANDOM PERMUTATIONS; SUPERVISED CLASSIFIERS; VARIABLE INTERACTION;

MEDICAL IMAGING;

DECISION TREES;

EID: 84864831381     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2012.6235507     Document Type: Conference Paper

References (16)

1. H J Cordell, "Detecting gene-gene interactions that underline human diseases," Nat Rev Genet, vol. 10, pp. 392-404, 2009.
2. R Diaz-Uriarte and S Alvarez de Andres, "Gene selection and classification of microarray data using random forest," BMC Bioinformatics, vol. 7, 2006.
3. Y Saeys, I Inza, and P Larranaga, "A review of feature selection techniques in bioinformatics," Bioinformatics, vol. 23, pp. 2507-2517, 2007.
4. K J Archer and R V Kimes, "Empirical characterization of random forest variable importance measures," Comp Statistics and Data Analysis, vol. 52, pp. 2249-2260, 2008.
5. R Raghuraj and S Lakshminarayanan, "VPMCD: Variable interaction modeling approach for class discrimination in biological systems," FEBS Letters, vol. 581, pp. 826-830, 2007.
6. E Carrizosa, B Martin-Barragan, and D R Morales, "Detecting relevant varaibles and interactions for classification in supervised classification," European Journal of Operational Research, vol. 213, pp. 260-269, 2011.
7. L Breiman and A Cutler, "Random forests," http://www.stat. berkeley.edu/~breiman/RandomForests/, Accessed February 2, 2011.
8. L Breiman, "Random forest," Machine Learning, vol. 45, pp. 5-32, 2001.
9. E Tuv, A Borisov, G Runger, and K Torkkola, "Feature selection with ensembles, artificial variables, and redundancy elimination," Journal of Machine Learning Research, vol. 10, pp. 1341-1366, 2009.
10. L Breiman, "Bagging predictors," Machine Learning, vol. 26, pp. 123- 140, 1996.
11. C Strobl, A L Boulesteix, T Kneib, T Augustin, and A Zeileis, "Conditional variable importance for random forests," BMC Bioinformatics, vol. 9, 2008.
12. K K Nicodemus, "Letter to the editor: On the stability and ranking of predictors from random forest variable importance measures," Briefings in Bioinformatics, vol. 12, pp. 369-373, 2011.
13. A Frank and A Asuncion, "UCI machine learning repository," http: //archive.ics.uci.edu/ml, 2010, Accessed August 31, 2011.
14. Y Amit and D Geman, "Shape quantization and recognition with randomized trees," Neural Computation, vol. 9, pp. 1545-1588, 1997.
15. G Jasso, "On Gini's mean difference and Gini's index of concentration," American Sociological Review, vol. 44, pp. 867-870, 1979.
16. L A Kurgan, K J Cios, and R Tadeusiewicz, "Knowledge discovery approach to automated cardiac SPECT diagnosis," Artificial Intelligence in Medicine, vol. 23/2, pp. 149-169, 2001.