A new hybrid metaheuristic for medical data classification

Authors:
Sarab AlMuhaideb;Mohamed El Bachir Menai
Affiliations:
Department of Computer Science, College of Computer and Information Sciences, King Saud University, 51178 Riyadh 11543, Saudi Arabia;Department of Computer Science, College of Computer and Information Sciences, King Saud University, 51178 Riyadh 11543, Saudi Arabia
Venue:
International Journal of Metaheuristics
Year:
2014

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Abstract

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.