A new hybrid metaheuristic for medical data classification
Sarab AlMuhaideb, Mohamed El Bachir Menaï · International Journal of Metaheuristics · 2014
The classification of medical data is a complex task. Medical diagnosis and/or prognosis can be modelled as classification tasks. A hybrid metaheuristic is introduced consisting of two phases; an ant colony optimisation (ACO) phase and a genetic algorithm (GA) phase. The population of the GA is initialised to decision lists constructed during the ACO phase using different subsets of the training data. The task of the GA is to optimise the decision lists obtained in terms of classification accuracy and model size. Results on a number of benchmark real-world medical datasets show the usefulness of the proposed approach.