Active feature selection in optic nerve data using support vector machine

Describes a data mining framework that aids in the process of finding an optimal set of features and its application into classification and detection of glaucoma from optic nerve data. The selection and evaluation of features were done using support vector machines. The search space for feature selection were reduced using an active feature sampling algorithm.