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HColonies: a new hybrid metaheuristic for medical data classification

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Abstract

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.

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Notes

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  5. http://weka.wikispaces.com/ARFF.

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Acknowledgements

This research project has been supported by a grant from the “Research Center of the Center for Female Scientific and Medical Colleges”, Deanship of Scientific Research, King Saud University. The authors would like to thank the anonymous reviewers for their valuable and constructive comments. Special thanks to Dr. Joaquin Derrac Rus from Cardiff University, UK, for his assistance in using KEEL. Also, an honorable mention goes to Dr. Pedro J. García Laencina from Universidad Politécnica de Cartagena, Spain; Dr. Iñaki Inza from University of the Basque Country, Spain; Dr. Bahriye Basturk Akay from Erciyes University, Turkey; Dr. Pat Langley from Stanford University, California; and Dr. Sotos Kotsiantis from University of Patras, Greece, for providing useful information for the project.

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  1. Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Central Saudi Arabia

    Sarab AlMuhaideb & Mohamed El Bachir Menai

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  1. Sarab AlMuhaideb
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Correspondence to Sarab AlMuhaideb.

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AlMuhaideb, S., Menai, M.E.B. HColonies: a new hybrid metaheuristic for medical data classification. Appl Intell 41, 282–298 (2014). https://doi.org/10.1007/s10489-014-0519-z

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