Ensemble Feature Ranking Applied to Medical Data

Abstract

Reduce the feature space in classification is a critical, although sensitive, task since it depends on a certain definition of relevance. Feature selection has been the motivation for many researchers. In medical datasets, relevant attributes are often unknown a priori. Feature selection provides the features that contribute most to the classification task per si, which should therefore be used by any classifier to produce a classification model. However, the dimension of the feature space may not allow the application of feature selection algorithms, due time and space complexity. In this work, we are concerned on the application of an efficient feature ranking algorithm for a given breast cancer dataset, that overcome the dimensionality of the data.