Aggregation Algorithms for Neural Networks Ensemble Construction

• Autores: Granitto, Pablo Miguel; Verdes, Pablo Fabián; Navone, Hugo Daniel; Ceccatto, Hermenegildo Alejandro
• Año de publicación: 2002
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; algorithms; neural networks aggregation
• Institución: Universidad Nacional de La Plata

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.
Sociedad Argentina de Informática e Investigación Operativa