Using Oblique Decision Trees for the Morphological Classification of   Galaxies
  We discuss the application of a class of machine learning algorithms known as decision trees to the process of galactic classification. In particular, we explore the application of oblique decision trees induced with different impurity measures to the problem of classifying galactic morphology data provided by Storrie-Lombardi et al.(1992).   Our results are compared to those obtained by a neural network classifier created by Storrie-Lombardi et al, and we show that the two methodologies are comparable. We conclude with a demonstration that the original data can be easily classified into less well-defined categories. 
E. Owens, R. E. Griffiths, and K. U. Ratnatunga (Johns Hopkins   University)