Feature selection is a data preprocessing step for classi cation and data mining tasks. Traditionally, feature selection is done by selecting a minimum number of features that det...
When selecting features with the sequential forward floating selection (SFFS), the "nesting effect" is avoided, which is a common phenomenon if the computationally less ...
Most of the prior work in biometric literature has only emphasized on the issue of feature extraction and classification. However, the critical issue of examining the usefulness of...
Wrapper-based feature selection is attractive because wrapper methods are able to optimize the features they select to the specific learning algorithm. Unfortunately, wrapper met...
Stability is an important yet under-addressed issue in feature selection from high-dimensional and small sample data. In this paper, we show that stability of feature selection ha...