  In this work, decision tree learning algorithms and fuzzy inferencing systems
are applied for galaxy morphology classification. In particular, the CART, the
C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable
classifiers are developed to distinguish between spiral galaxies, elliptical
galaxies or star/unknown galactic objects. Morphology information for the
training and testing datasets is obtained from the Galaxy Zoo project while the
corresponding photometric and spectra parameters are downloaded from the SDSS
DR7 catalogue.
