Multi-objective evolutionary algorithms for fuzzy classification in survival prediction

A novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients is presented and it is concluded that ENORA outperforms niched pre-selection and NSGA-II algorithms.

Abstract

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.