7 search results - page 1 / 2 » An Experimental Study on Rotation Forest Ensembles |
MCS
2007
Springer
13 years 11 months ago
2007
Springer
Rotation Forest is a recently proposed method for building classifier ensembles using independently trained decision trees. It was found to be more accurate than bagging, AdaBoost...
CAEPIA
2003
Springer
13 years 10 months ago
2003
Springer
A new method for ensemble generation is presented. It is based on grouping the attributes in dierent subgroups, and to apply, for each group, an axis rotation, using Principal Com...
ICDAR
2007
IEEE
13 years 11 months ago
2007
IEEE
In the Pattern Recognition field, growing interest has been shown in recent years for Multiple Classifier Systems and particularly for Bagging, Boosting and Random Subspaces. Th...
AUSDM
2006
Springer
13 years 8 months ago
2006
Springer
In response to the rapid development of DNA Microarray technology, many classification methods have been used for Microarray classification. SVMs, decision trees, Bagging, Boostin...
CIDM
2009
IEEE
13 years 11 months ago
2009
IEEE
— Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ens...