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ICAPR
2009
Springer
15 years 4 months ago
2009
Springer
— Feature selection and ensemble classification increase system efficiency and accuracy in machine learning, data mining and biomedical informatics. This research presents an ana...
ICIC
2009
Springer
14 years 7 months ago
2009
Springer
In this paper we present a study on the Random Forest (RF) family of ensemble methods. From our point of view, a "classical" RF induction process presents two main drawba...
PR
2010
14 years 7 months ago
2010
The performance of m-out-of-n bagging with and without replacement in terms of the sampling ratio (m/n) is analyzed. Standard bagging uses resampling with replacement to generate ...
PRL
2008
14 years 9 months ago
2008
Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the comb...
PAKDD
2010
ACM
15 years 2 months ago
2010
ACM
Abstract. It is well known that diversity among component classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods achieve this goal through resam...