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» Experiments on Ensembles with Missing and Noisy Data
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MCS
2004
Springer
13 years 10 months ago
Experiments on Ensembles with Missing and Noisy Data
Abstract. One of the potential advantages of multiple classifier systems is an increased robustness to noise and other imperfections in data. Previous experiments on classificati...
Prem Melville, Nishit Shah, Lilyana Mihalkova, Ray...
MCS
2007
Springer
13 years 10 months ago
Random Feature Subset Selection for Ensemble Based Classification of Data with Missing Features
Abstract. We report on our recent progress in developing an ensemble of classifiers based algorithm for addressing the missing feature problem. Inspired in part by the random subsp...
Joseph DePasquale, Robi Polikar
BIBE
2007
IEEE
153views Bioinformatics» more  BIBE 2007»
13 years 6 months ago
Combined expression data with missing values and gene interaction network analysis: a Markovian integrated approach
—DNA microarray technologies provide means for monitoring in the order of tens of thousands of gene expression levels quantitatively and simultaneously. However data generated in...
Juliette Blanchet, Matthieu Vignes
ICCV
2005
IEEE
14 years 6 months ago
An Ensemble Prior of Image Structure for Cross-Modal Inference
In cross-modal inference, we estimate complete fields from noisy and missing observations of one sensory modality using structure found in another sensory modality. This inference...
S. Ravela, Antonio B. Torralba, William T. Freeman
KDD
2005
ACM
182views Data Mining» more  KDD 2005»
14 years 4 months ago
Making holistic schema matching robust: an ensemble approach
The Web has been rapidly "deepened" by myriad searchable databases online, where data are hidden behind query interfaces. As an essential task toward integrating these m...
Bin He, Kevin Chen-Chuan Chang