Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Machine Learning
Machine Learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Combining Decision Trees Based on Imprecise Probabilities and Uncertainty Measures
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Hi-index | 0.00 |
Random forest models [1] consist of an ensemble of randomized decision trees. It is one of the best performing classification models. With this idea in mind, in this section we introduced a random split operator based on a Bayesian approach for building a random forest. The convenience of this split method for constructing ensembles of classification trees is justified with an error bias-variance decomposition analysis. This new split operator does not clearly depend on a parameter K as its random forest's counterpart, and performs better with a lower number of trees.