title: Growing a classification tree using the apparent misclassification rate.
abstract: A method to determine the size of a classification tree is proposed. This method is based on the change of the apparent misclassification rate (AMR) of the tree at each growing stage. The method is simple and fast compared to the other classification tree methods, which are based on minimizing a cost complexity function. To test the method, it was used to classify species of fungi, and the results are in good agreement with those obtained by linear discriminant analysis. Also, 21 proteins with known structures and functions were classified using the proposed method. For this purpose the coefficient of variation for several properties of the secondary structures of these proteins has been used. Again, the results were in good agreement with the classification obtained previously using dynamic programming.
authors.affiliation: Laboratory of Mathematical Biology, National Institute for Medical Research, The Ridgeway, Mill Hill, London, UK.
authors.forename: E
authors.initials: E
authors.lastname: Zintzaras
authors.forename: N P
authors.initials: NP
authors.lastname: Brown
authors.forename: A
authors.initials: A
authors.lastname: Kowald
mesh_terms: D000465:Algorithms; D002965:Classification / Q000379:methods*; D016002:Discriminant Analysis; D005658:Fungi / Q000145:classification; D017433:Protein Structure, Secondary; D011506:Proteins / Q000145:classification
chemical_list: D011506:Proteins