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» On learning algorithm selection for classification
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BMCBI
2007
143views more  BMCBI 2007»
13 years 7 months ago
Gene selection for classification of microarray data based on the Bayes error
Background: With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, ...
Ji-Gang Zhang, Hong-Wen Deng
ICDM
2010
IEEE
228views Data Mining» more  ICDM 2010»
13 years 5 months ago
Multi-label Feature Selection for Graph Classification
Nowadays, the classification of graph data has become an important and active research topic in the last decade, which has a wide variety of real world applications, e.g. drug acti...
Xiangnan Kong, Philip S. Yu
BMCBI
2007
173views more  BMCBI 2007»
13 years 7 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
ML
2002
ACM
180views Machine Learning» more  ML 2002»
13 years 7 months ago
Gene Selection for Cancer Classification using Support Vector Machines
Isabelle Guyon, Jason Weston, Stephen Barnhill, Vl...
NIPS
2004
13 years 9 months ago
Instance-Specific Bayesian Model Averaging for Classification
Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for l...
Shyam Visweswaran, Gregory F. Cooper