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» Variable selection using random forests
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CATA
2009
14 years 10 months ago
Nearest Shrunken Centroid as Feature Selection of Microarray Data
The nearest shrunken centroid classifier uses shrunken centroids as prototypes for each class and test samples are classified to belong to the class whose shrunken centroid is nea...
Myungsook Klassen, Nyunsu Kim
IDA
2007
Springer
15 years 3 months ago
Combining Bagging and Random Subspaces to Create Better Ensembles
Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples of the training data (as in b...
Pance Panov, Saso Dzeroski
TDP
2010
124views more  TDP 2010»
14 years 8 months 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
PR
2011
14 years 15 days ago
Hierarchical annotation of medical images
In this paper, we describe an approach for the automatic medical annotation task of the 2008 CLEF cross-language image retrieval campaign (ImageCLEF). The data comprise 12076 full...
Ivica Dimitrovski, Dragi Kocev, Suzana Loskovska, ...
ICISP
2010
Springer
15 years 2 months ago
Classification of High-Resolution NMR Spectra Based on Complex Wavelet Domain Feature Selection and Kernel-Induced Random Forest
High-resolution nuclear magnetic resonance (NMR) spectra contain important biomarkers that have potentials for early diagnosis of disease and subsequent monitoring of its progressi...
Guangzhe Fan, Zhou Wang, Seoung Bum Kim, Chivalai ...