Hyperbox clustering with Ant Colony Optimization (HACO) method and its application to medical risk profile recognition |
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| Authors: | GN Ramos Y Hatakeyama F Dong K Hirota |
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| Affiliation: | 1. INTEC (CONICET and Universidad Nacional del Litoral), Güemes 3450, (3000) Santa Fe, Argentina;2. FRP-UTN (Facultad Regional Paraná – Universidad Tecnológica Nacional), Almafuerte 1033 - (3100) Paraná, Argentina;3. FRSF-UTN (Facultad Regional Santa Fe – Universidad Tecnológica Nacional), Lavaisse 610, (3000) Santa Fe, Argentina;1. State Key Lab of Industrial Control Technology, Institute of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China;2. School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China |
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| Abstract: | A clustering method, called HACO (Hyperbox clustering with Ant Colony Optimization), is proposed for classifying unlabeled data using hyperboxes and an ant colony meta-heuristic. It acknowledges the topological information (inherently associated to classification) of the data while looking in a small search space, providing results with high precision in a short time. It is validated using artificial 2D data sets and then applied to a real medical data set, automatically extracting medical risk profiles, a laborious operation for doctors. Clustering results show an improvement of 36% in accuracy and 7 times faster processing time when compared to the usual ant colony optimization approach. It can be further extended to hyperbox shape optimization (fine tune accuracy), automatic parameter setting (improve usability), and applied to diagnosis decision support systems. |
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