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

Claudia Plant | Zendy; Christian Böhm | Zendy; B. Tilg | Zendy; Christian Baumgärtner | Zendy

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

Author(s) -

Claudia Plant,

Christian Böhm,

B. Tilg,

Christian Baumgärtner

Publication year - 2006

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btl027

Subject(s) - outlier , classifier (uml) , computer science , artificial intelligence , data mining , pattern recognition (psychology) , one class classification , class (philosophy) , cluster (spacecraft) , object (grammar) , algorithm , machine learning , programming language

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.

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