Multi-objective Feature Selection with NSGA II
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
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This paper describes a hybrid method for supervised training of multivariate regression systems. The proposed methodology relies on supervised clustering with genetic algorithms and local learning. Genetic Algorithm driven Clustering (GAdC) offers certain advantages related to robustness, generalization performance, feature selection, explanative behavior and the additional flexibility of defining the error function and the regularization constraints. In this contribution, we present the use of GAdC for prediction of algae distributions. We highlight one of the advantages of this method namely, the use of scalars to obtain the sequence in which the prediction of algae distributions should be calculated. Using this sequence leads to an improvement of the prediction.