Ensemble learning using multi-objective evolutionary algorithms

Ensemble learning using multi-objective evolutionary algorithms

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Article ID: iaor20081416
Country: United Kingdom
Volume: 5
Issue: 4
Start Page Number: 417
End Page Number: 445
Publication Date: Dec 2006
Journal: Journal of Mathematical Modelling and Algorithms
Authors: ,
Keywords: heuristics: genetic algorithms, neural networks, programming: multiple criteria
Abstract:

Multi-objective evolutionary algorithms for the construction of neural ensembles is a relatively new area of research. We recently proposed an ensemble learning algorithm called DIVACE (DIVerse and ACcurate Ensemble learning algorithm). It was shown that DIVACE tries to find an optimal trade-off between diversity and accuracy as it searches for an ensemble for some particular pattern recognition task by treating these two objectives explicitly separately. A detailed discussion of DIVACE together with further experimental studies form the essence of this paper. A new diversity measure which we call Pairwise Failure Crediting (PFC) is proposed. This measure forms one of the two evolutionary pressures being exerted explicitly in DIVACE. Experiments with this diversity measure as well as comparisons with previously studied approaches are hence considered. Detailed analysis of the results shows that DIVACE, as a concept, has promise.

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