54 search results - page 4 / 11 » An Empirical Evaluation of Bagging and Boosting |
JMLR
2010
13 years 2 months ago
2010
Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal...
ICML
1999
IEEE
14 years 8 months ago
1999
IEEE
Lbr is a lazy semi-naive Bayesian classi er learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classi cation. To classify a test exa...
MCS
2004
Springer
14 years 24 days ago
2004
Springer
We experimentally evaluate bagging and six other randomization-based approaches to creating an ensemble of decision-tree classifiers. Bagging uses randomization to create multipl...
BMCBI
2004
13 years 7 months ago
2004
Background: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination w...
ICML
2003
IEEE
14 years 8 months ago
2003
IEEE
This paper explores the problem of how to construct lazy decision tree ensembles. We present and empirically evaluate a relevancebased boosting-style algorithm that builds a lazy ...