Thesis - Instituto de Telecomunicações

5.2. USER TUNED FEATURE SELECTION 97reduced since in order to get a particular estimation the set of points is much smaller thanthe original used by the KDE.Figure 5.2: Reduced set density estimation. A solid line represents the pdf of the modelthat generated the data. The selected samples with the height proportional to the weightof the samples are marked with an x. The dashed line is the estimated pdf.An example of RSDE modeling is presented in figure 5.2. The data used in the exampleis the same used in figure 5.1. The weight associated with the sample is depicted by thevalue of the sample in the vertical axis.5.2 User Tuned Feature SelectionThe total feature set generated by the feature extraction procedure is analyzed in thisprocessing stage in order to select the subset of features that best discriminates betweenthe several users, in a authentication framework. Feature selection methods can be classifiedin one of the following general classes [115, 218]:1. Filter methods - in these methods the data structure is analysed in order to detectindependence among the several features. Several measurements are defined to extractinformation about the data structure [71].2. Wrapper methods - the classifier, that is being used in the problem, is used to give someinsight on the quality of the features subsets [133], supporting the search procedure.