DocumentCode :
2220572
Title :
Reduced ensemble size stacking [ensemble learning]
Author :
Rooney, Niall ; Patterson, David ; Nugent, Chris
Author_Institution :
Ulster Univ., UK
fYear :
2004
fDate :
15-17 Nov. 2004
Firstpage :
266
Lastpage :
271
Abstract :
We investigate an algorithmic extension to the technique of stacked regression that prunes the size of a homogeneous ensemble set based on a consideration of the accuracy and diversity of the set members. We show that the pruned ensemble set is as accurate on average over the data-sets tested as the nonpruned version, which provides benefits in terms of its application efficiency and reduced complexity of the ensemble.
Keywords :
computational complexity; data structures; learning (artificial intelligence); regression analysis; set theory; pruned ensemble set; reduced ensemble size stacking; stacked regression; Bagging; Character generation; Decision trees; Diversity reception; Network topology; Neural networks; Predictive models; Sampling methods; Stacking; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2004. ICTAI 2004. 16th IEEE International Conference on
ISSN :
1082-3409
Print_ISBN :
0-7695-2236-X
Type :
conf
DOI :
10.1109/ICTAI.2004.105
Filename :
1374197
Link To Document :
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