A note on the height of binary search trees
Journal of the ACM (JACM)
Applications of the theory of records in the study of random trees
Acta Informatica
Machine Learning
Shape quantization and recognition with randomized trees
Neural Computation
Machine Learning
The Journal of Machine Learning Research
Evaluating the Impact of Missing Data Imputation
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Ensemble Approach for the Classification of Imbalanced Data
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Variable selection using random forests
Pattern Recognition Letters
Mining data with random forests: A survey and results of new tests
Pattern Recognition
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Learning with ensembles of randomized trees: new insights
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Analysis of a random forests model
The Journal of Machine Learning Research
Random Forest Classifier Based ECG Arrhythmia Classification
International Journal of Healthcare Information Systems and Informatics
The Journal of Machine Learning Research
Going-concern prediction using hybrid random forests and rough set approach
Information Sciences: an International Journal
A new variable importance measure for random forests with missing data
Statistics and Computing
| Hi-index | 0.00 |
In the last years of his life, Leo Breiman promoted random forests for use in classification. He suggested using averaging as a means of obtaining good discrimination rules. The base classifiers used for averaging are simple and randomized, often based on random samples from the data. He left a few questions unanswered regarding the consistency of such rules. In this paper, we give a number of theorems that establish the universal consistency of averaging rules. We also show that some popular classifiers, including one suggested by Breiman, are not universally consistent.