An AUC-based permutation variable importance measure for random forests
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[1] Stan Matwin,et al. Machine Learning for the Detection of Oil Spills in Satellite Radar Images , 1998, Machine Learning.
[2] Chao Chen,et al. Using Random Forest to Learn Imbalanced Data , 2004 .
[3] Kristin K. Nicodemus,et al. Letter to the Editor: On the stability and ranking of predictors from random forest variable importance measures , 2011, Briefings Bioinform..
[4] James J. Chen,et al. Class-imbalanced classifiers for high-dimensional data , 2013, Briefings Bioinform..
[5] Taghi M. Khoshgoftaar,et al. An Empirical Study of Learning from Imbalanced Data Using Random Forest , 2007, 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007).
[6] Anne-Laure Boulesteix,et al. AUC-RF: A New Strategy for Genomic Profiling with Random Forest , 2011, Human Heredity.
[7] James D. Malley,et al. Predictor correlation impacts machine learning algorithms: implications for genomic studies , 2009, Bioinform..
[8] Taghi M. Khoshgoftaar,et al. Experimental perspectives on learning from imbalanced data , 2007, ICML '07.
[9] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[10] Yan V. Sun,et al. Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests , 2007, BMC proceedings.
[11] Carolin Strobl,et al. The behaviour of random forest permutation-based variable importance measures under predictor correlation , 2010, BMC Bioinformatics.
[12] Daniel S. Myers,et al. Simple statistical models predict C-to-U edited sites in plant mitochondrial RNA , 2004, BMC Bioinformatics.
[13] BMC Bioinformatics , 2005 .
[14] Taghi M. Khoshgoftaar,et al. Knowledge discovery from imbalanced and noisy data , 2009, Data Knowl. Eng..
[15] K. Hornik,et al. Unbiased Recursive Partitioning: A Conditional Inference Framework , 2006 .
[16] Nathalie Japkowicz,et al. The class imbalance problem: A systematic study , 2002, Intell. Data Anal..
[17] D. Rujescu,et al. Evidence of statistical epistasis between DISC1, CIT and NDEL1 impacting risk for schizophrenia: biological validation with functional neuroimaging , 2010, Human Genetics.
[18] Joseph H. Callicott,et al. Erratum to: Evidence of statistical epistasis between DISC1, CIT and NDEL1 impacting risk for schizophrenia: biological validation with functional neuroimaging , 2010, Human Genetics.
[19] JapkowiczNathalie,et al. The class imbalance problem: A systematic study , 2002 .
[20] Taeho Jo,et al. A Multiple Resampling Method for Learning from Imbalanced Data Sets , 2004, Comput. Intell..
[21] Alexander R. Pico,et al. Pathway Analysis of Single-Nucleotide Polymorphisms Potentially Associated with Glioblastoma Multiforme Susceptibility Using Random Forests , 2008, Cancer Epidemiology Biomarkers & Prevention.
[22] Carolin Strobl,et al. Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations , 2012, Briefings Bioinform..
[23] J. Carulli,et al. A genome-wide screen of gene–gene interactions for rheumatoid arthritis susceptibility , 2011, Human Genetics.
[24] M. Pepe. The Statistical Evaluation of Medical Tests for Classification and Prediction , 2003 .
[25] Eric W. T. Ngai,et al. Customer churn prediction using improved balanced random forests , 2009, Expert Syst. Appl..
[26] Rok Blagus,et al. Class prediction for high-dimensional class-imbalanced data , 2010, BMC Bioinformatics.
[27] Hewijin Christine Jiau,et al. Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem , 2006 .
[28] Stephen J Sawcer,et al. Variation within DNA repair pathway genes and risk of multiple sclerosis. , 2010, American journal of epidemiology.
[29] Achim Zeileis,et al. Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.
[30] Tom Fawcett,et al. Adaptive Fraud Detection , 1997, Data Mining and Knowledge Discovery.
[31] Anne-Laure Boulesteix,et al. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics , 2012, WIREs Data Mining Knowl. Discov..
[32] Mohammad Khalilia,et al. Predicting disease risks from highly imbalanced data using random forest , 2011, BMC Medical Informatics Decis. Mak..
[33] K. Hornik,et al. party : A Laboratory for Recursive Partytioning , 2009 .
[34] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.