HColonies: a new hybrid metaheuristic for medical data classification

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 41; no. 1; pp. 282 - 298
• Main Authors: AlMuhaideb, Sarab, Menai, Mohamed El Bachir
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.07.2014; Kluwer; Springer Nature B.V
• Subjects: Accuracy; Algorithmics. Computability. Computer arithmetics; Ant colony optimization; Applied sciences; Artificial Intelligence; Bees; Benchmarking; Biological and medical sciences; Classification; Computer Science; Computer science; control theory; systems; Computerized, statistical medical data processing and models in biomedicine; Datasets; Decision trees; Exact sciences and technology; Food; Foods; Foraging behavior; Genetic algorithms; Health care; Heuristic methods; Lists; Machines; Manufacturing; Mechanical Engineering; Medical; Medical computing and teaching; Medical sciences; Neural networks; Optimization algorithms; Pheromones; Processes; Swarm intelligence; Tasks; Theoretical computing; Medical data; Sequential covering; Ant colony optimization; Hybrid metaheuristics; Artificial bee colony; Classification; Artificial life; Heuristic method; Swarm intelligence; Experimental study; Modeling; Metamodel
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-014-0519-z

Medical data feature a number of characteristics that make their classification a complex task. Yet, the societal significance of the subject and the computational challenge it presents has caused the classification of medical datasets to be a popular research area. A new hybrid metaheuristic is presented for the classification task of medical datasets. The hybrid ant–bee colonies (HColonies) consists of two phases: an ant colony optimization (ACO) phase and an artificial bee colony (ABC) phase. The food sources of ABC are initialized into decision lists, constructed during the ACO phase using different subsets of the training data. The task of the ABC is to optimize the obtained decision lists. New variants of the ABC operators are proposed to suit the classification task. Results on a number of benchmark, real-world medical datasets show the usefulness of the proposed approach. Classification models obtained feature good predictive accuracy and relatively small model size.