Aggregation algorithms for neural network ensemble construction

How to generate and aggregate base learners to have optimal ensemble generalization capabilities is an important questions in building composite regression/classification machines. We present here an evaluation of several algorithms for artificial neural networks aggregation in the regression settin...

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Bibliographic Details
Published inVII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings pp. 178 - 183
Main Authors Granitto, P.M., Verdes, P.F., Navone, H.D., Ceccatto, H.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Aggregates
Artificial neural networks
Bagging
Benchmark testing
Boosting
Computer networks
Concurrent computing
Neural networks
Proposals
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Summary:How to generate and aggregate base learners to have optimal ensemble generalization capabilities is an important questions in building composite regression/classification machines. We present here an evaluation of several algorithms for artificial neural networks aggregation in the regression settings, including new proposals and comparing them with standard methods in the literature. We also discuss a potential problem with sequential algorithms: the non frequent but damaging selection through their heuristics of particularly bad ensemble members. We show that one can cope with this problem by allowing individual weighting of aggregate members. Our algorithms and their weighted modifications are favorably tested against other methods in the literature, producing a performance improvement on the standard statistical databases used as benchmarks.
ISBN:9780769517094
0769517099
DOI:10.1109/SBRN.2002.1181466