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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Hybrid Metaheuristics for Medical Data Classification
verfasst von : Sarab Al-Muhaideb, Mohamed El Bachir Menai
Erschienen in: Hybrid Metaheuristics
Verlag: Springer Berlin Heidelberg
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Medical data exhibit certain features that make their classification stand out as a distinct field of research. Several medical classification tasks exist, among which medical diagnosis and prognosis are most common. Deriving a medical classification is a complex task. In particular, the rule–discovery problem is NP-hard. Identifying the most suitable strategy for a particular medical classification problem along with its optimal parameters is no less difficult. Heuristics and meta-heuristics are normally applied to approximate its solution. This chapter reviews hybrid meta-heuristics for medical data classification task, particularly diagnosis and prognosis, and their application to model selection, including parameter optimization and feature subset selection.