54 search results - page 3 / 11 » An Empirical Evaluation of Bagging and Boosting |
66
Voted
JMLR
2010
14 years 4 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
15 years 10 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
15 years 10 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...
96
Voted
PAMI
2007
14 years 9 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...
67
Voted
ICONIP
2008
14 years 11 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...