Material Detail

This video was recorded at Workshop on Subspace, Latent Structure and Feature Selection Techniques: Statistical and Optimisation Perspectives, Bohinj 2005. Many feature selection algorithms are limited in that they attempt to identify relevant feature subsets by examining the features individually. This paper introduces a technique for determining feature relevance using the average information gain achieved during the construction of decision tree ensembles. The technique introduces a node complexity measure and a statistical method for updating the feature sampling distribution based upon confidence intervals to control the rate of convergence. Experiments demonstrate the potential of this method for feature selection and subspace identification.

Disciplines:

• Science and Technology  / Computer Science  / Programming & Programming Languages