METODE KLASIFIKASI BERSTRUKTUR POHON DENGAN ALGORITMA CRUISE

Yasmin Erika F.
Andriansyah Andriansyah

Statistika
Vol 3, No 1 (2003)

Two univariate split methods are proposed for the construction of classification trees with multyway splits namedCRUISE (Classification Rule with Unbiased Interaction Selection and Estimation). A major strenght of the univariate splitmethods is that they have negligible bias in variable selection, both when the variables differ in the number of splits they offerand when they differ in number of missing values. This is an advantage because inference from the tree sructures can beadversely affected by selection bias. These methods also improve interpretability of trees by reducing tree depht.Application of CRUISE algorithms to Fisher’s Iris data is to predict the variety of an Iris flower based on its petal and sepallenght and widht. Results show that it only takes one variable to do so. Therefoce, the new methods are highly competitive interms of computational speed and classification accuracy of future observation.