New machine learning algorithm: random forest

Authors:
Yanli Liu;Yourong Wang;Jian Zhang
Affiliations:
Basic Teaching Department, Tangshan College, Tangshan, Hebei, China;Basic Teaching Department, Tangshan College, Tangshan, Hebei, China;Basic Teaching Department, Tangshan College, Tangshan, Hebei, China
Venue:
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Year:
2012

Citing 9
Cited 0

Bagging predictors

Machine Learning
Shape quantization and recognition with randomized trees

Neural Computation
The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Method To Estimate Bagging‘s Generalization Error

Machine Learning
Prediction games and arcing algorithms

Neural Computation
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Machine Learning
Random Forests

Machine Learning
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Random decision forests

ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

This Paper gives an introduction of Random Forest. Random Forest is a new Machine Learning Algorithm and a new combination Algorithm. Random Forest is a combination of a series of tree structure classifiers. Random Forest has many good characters. Random Forest has been wildly used in classification and prediction, and used in regression too. Compared with the traditional algorithms Random Forest has many good virtues. Therefore the scope of application of Random Forest is very extensive.