Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
An Efficient Method To Estimate Bagging‘s Generalization Error
Machine Learning
Machine Learning
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An empirical evaluation of bagging and boosting
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Empirical characterization of random forest variable importance measures
Computational Statistics & Data Analysis
Additive Groves of Regression Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
Feature Ranking Ensembles for Facial Action Unit Classification
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Bundling classifiers by bagging trees
Computational Statistics & Data Analysis
Stopping criteria for ensemble-based feature selection
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
An ensemble dependence measure
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Small-sample error estimation for bagged classification rules
EURASIP Journal on Advances in Signal Processing - Special issue on genomic signal processing
Over-Fitting in ensembles of neural network classifiers within ECOC frameworks
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Supervising random forest using attribute interaction networks
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Out-of-bag discriminative graph mining
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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For two-class datasets, we provide a method for estimating the generalization error of a bag using out-of-bag estimates. In bagging, each predictor (single hypothesis) is learned from a bootstrap sample of the training examples; the output of a bag (a set of predictors) on an example is determined by voting. The out-of-bag estimate is based on recording the votes of each predictor on those training examples omitted from its bootstrap sample. Because no additional predictors are generated, the out-of-bag estimate requires considerably less time than 10-fold cross-validation. We address the question of how to use the out-of-bag estimate to estimate generalization error on two-class datasets. Our experiments on several datasets show that the out-of-bag estimate and 10-fold cross-validation have similar performance, but are both biased. We can eliminate most of the bias in the out-of-bag estimate and increase accuracy by incorporating a correction based on the distribution of the out-of-bag votes.