Enhancing instance-based classification with local density: a new algorithm for classifying unbalanced biomedical data

Claudia Plant, Christian Böhm, Bernhard Tilg, Christian Baumgartner

Abstract: Classification is an important data mining task in biomedicine. In particular, classification on biomedical data often claims the separation of pathological and healthy samples with highest discriminatory performance for diagnostic issues. Even more important than the overall accuracy is the balance of a classifier, particularly if datasets of unbalanced class size are examined.