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Complexity is your problem, classifiers may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The classifiers concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, artificial intelligence, data mining and modeling). One field that is taking increasing notice of classifiers is epidemiology, where there is a growing demand for powerful tools to facilitate etiological discovery. This paper aims to provide an accessible foundation for researchers of different backgrounds interested in selecting or developing their own classifiers. Included is a simple yet thorough introduction, and a roadmap of algorithmic components, emphasizing differences in different classifiers implementations. |
