Multi-objective evolutionary algorithms for fuzzy classification in survival prediction

Conclusions: Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization.

Source: Artificial Intelligence in Medicine - February 12, 2014 Category: Bioinformatics Authors: Fernando Jiménez, Gracia Sánchez, José M. Juárez Tags: Research Articles Source Type: research