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ICDM
2010
IEEE
108views Data Mining» more  ICDM 2010»
15 years 8 days ago
Assessing Data Mining Results on Matrices with Randomization
Abstract--Randomization is a general technique for evaluating the significance of data analysis results. In randomizationbased significance testing, a result is considered to be in...
Markus Ojala
CATA
2008
15 years 3 months ago
Investigation of Random Forest Performance with Cancer Microarray Data
The diagnosis of cancer type based on microarray data offers hope that cancer classification can be highly accurate for clinicians to choose the most appropriate forms of treatmen...
Myungsook Klassen, Matt Cummings, Griselda Saldana
SADM
2011
14 years 9 months ago
Random survival forests for high-dimensional data
: Minimal depth is a dimensionless order statistic that measures the predictiveness of a variable in a survival tree. It can be used to select variables in high-dimensional problem...
Hemant Ishwaran, Udaya B. Kogalur, Xi Chen, Andy J...
PKDD
2004
Springer
116views Data Mining» more  PKDD 2004»
15 years 7 months ago
Random Matrices in Data Analysis
We show how carefully crafted random matrices can achieve distance-preserving dimensionality reduction, accelerate spectral computations, and reduce the sample complexity of certai...
Dimitris Achlioptas
TDP
2010
124views more  TDP 2010»
15 years 20 days ago
Random Forests for Generating Partially Synthetic, Categorical Data
Abstract. Several national statistical agencies are now releasing partially synthetic, public use microdata. These comprise the units in the original database with sensitive or ide...
Gregory Caiola, Jerome P. Reiter