Aggregation algorithms for neural network ensemble construction
- Resource Type
- Conference
- Authors
- Granitto, P.M.; Verdes, P.F.; Navone, H.D.; Ceccatto, H.A.
- Source
- VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings. Brazilian symposium on neural networks Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on. :178-183 2002
- Subject
- Computing and Processing
Components, Circuits, Devices and Systems
Signal Processing and Analysis
Neural networks
Aggregates
Artificial neural networks
Bagging
Boosting
Proposals
Databases
Benchmark testing
Computer networks
Concurrent computing
- Language
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.