54 search results - page 3 / 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 includes a collecti...
ICML
1999
IEEE
14 years 8 months ago
1999
IEEE
AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boo...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
In this paper we perform an empirical evaluation of supervised learning on highdimensional data. We evaluate performance on three metrics: accuracy, AUC, and squared loss and stud...
PAMI
2007
13 years 7 months ago
2007
Abstract—We experimentally evaluate bagging and seven other randomizationbased approaches to creating an ensemble of decision tree classifiers. Statistical tests were performed o...
ICONIP
2008
13 years 9 months ago
2008
In this paper, we present the performance of machine learning-based methods for detection of phishing sites. We employ 9 machine learning techniques including AdaBoost, Bagging, S...