DocumentCode :
3443924
Title :
An ensemble learning algorithm based on Lasso selection
Author :
Chen, Kai ; Jin, Yang
Author_Institution :
Sch. of Stat., Renmin Univ. of China, Beijing, China
Volume :
1
fYear :
2010
fDate :
29-31 Oct. 2010
Firstpage :
617
Lastpage :
620
Abstract :
Ensemble learning, especially selective ensemble learning is now becoming more and more popular in the field of machine learning. This paper introduces a new ensemble algorithm, named Lasso-Bagging Trees ensemble algorithm. This algorithm is in order to improve the whole learning ability, which is a combination of tree predictors and this method chooses and ensembles trees based on the shrinkage estimation of lasso technology. Compared with a series of other learning algorithms, it demonstrates better generalization ability and higher efficiency.
Keywords :
decision trees; estimation theory; generalisation (artificial intelligence); learning (artificial intelligence); statistical analysis; Lasso Bagging trees ensemble algorithm; generalization; learning ability; machine learning; selective ensemble learning; shrinkage estimation; tree predictor; Algae; Artificial intelligence; Bagging; Bootstrap; Decision Tree; Selective Ensemble;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-6582-8
Type :
conf
DOI :
10.1109/ICICISYS.2010.5658515
Filename :
5658515
Link To Document :
بازگشت