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A combinatorial approach to the classification problem

Affiliation:	1. Department of Cardiovascular Sciences, University of Leicester, Leicester, UK;2. Department of Cardiothoracic Surgery, Royal Papworth Hospital, Cambridge, UK;3. School of Medicine, Royal College of Surgeons in Ireland, Busaiteen, Bahrain;4. Faculty of Medicine, St Mary’s Hospital, London, UK;5. Faculty of Medicine, Health and Life Science, Swansea Medical School, Swansea, UK;6. Department of Plastic Surgery, Nottingham City Hospital, Nottingham, UK;7. Department of Cardiothoracic Surgery, Liverpool Heart and Chest Hospital, Liverpool, UK;8. School of Medicine, Faculty of Health and Life Science, Ain Shams University, Cairo, Egypt;1. Assistant Professor,Computer Engg. Department,Don Bosco College of Engineering,Margao-Goa,403604. India;2. Assistant Professor,Computer Engg. Department,Don Bosco College of Engineering,Margao-Goa,403604. India;1. Hearing Systems Group, Department of Electrical Engineering, Technical University of Denmark, Ørsteds Plads, Building 352, 2800 Kgs. Lyngby, Denmark;2. Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegaard Allé 30, 2650 Hvidovre, Denmark

Abstract:	We study the two-group classification problem which involves classifying an observation into one of two groups based on its attributes. The classification rule is a hyperplane which misclassifies the fewest number of observations in the training sample. Exact and heuristic algorithms for solving the problem are presented. Computational results confirm the efficiency of this approach.

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