Feature subset selection is the process of choosing the variables that are important for the classification stage from the original feature space. Feature selection is an important task for almost any pattern recognition problem (Webb, 1999). This procedure is aimed to derive as many discriminative cues as possible whilst removing the redundant and irrelevant information which may degrade the recognition rate. Furthermore, feature selection does not only reduce the cost of recognition by reducing the dimensionality of the feature space, but also offers an improved classification performance through a more stable and compact representation (Jain 1982). It is practically infeasible to run an exhaustive search for all the  possible combinations of features in order to obtain the optimal subset for recognition due to the high dimensionality of the feature space.  For this reason, it is recommended to use a feature selection algorithm as  the Adaptive Sequential Forward Floating Selection (ASFFS)  search algorithm (Pudil 1994).

The feature selection procedure fundamentally relies on an evaluation function that determines the usefulness of each feature in order to derive the ideal subset of features for the classification phase. For every feature or set of features generated by the feature selection algorithm, an evaluation criterion is called to measure the discriminative ability of the set of features to distinguish different subjects (Dash 1997). A number of methods (Mowbray, 2003) rely mainly on statistical metric measures which are based on the scatter or distribution of the training samples in the feature space such as the Bhattacharyya metric. These methods aim to find the features which minimize the overlap between the different classes as well as the inner-class scatter.

This entry was posted on Wednesday, April 16th, 2008 at 3:46 am and is filed under Research. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.