Thesis - Instituto de Telecomunicações

98 CHAPTER 5. FEATURE SELECTION AND CLASSIFICATION3. Embedded methods - in this class of feature selection methods the classifier is anintegral part of the feature selection process, therefore the features to be used aredecided during the learning stage [22].We implemented a feature selection based on a wrapper approach, using the SequentialForward Search feature selection procedure in a user tuned scenario.5.2.1 Feature Selection ImplementationFor the purpose of feature selection we consider that we have a classifier system that receivesa subset of features and returns the EER of the system.Features selection is thereforedesigned in the context of user authentication. The employed classifiers will be explainedin the next sections.As a first approach, we searched for a set of features that could be used for all users. Weused a greedy search algorithm, called Sequential Forward Search [206, 217], schematicallydescribed in algorithm 2.Algorithm 2: Sequential Forward Search Feature Selection.Input: feature-vector/* feature-vector the complete feature vector */1 last-eer ← 12 feature-subset-vector ← []3 while true do4 vector-eer ← [0, ··· , 0] nf5 for i =1...n f do6 feature-test-vector ← feature-vector i ∪ feature-subset-vector7 vector-eer i = TestClassifier(feature-test-vector)8 end9 if Min(vector-eer) > last-eer thenReturn(feature-subset-vector)endbest-feature ← ArgMin(vector-eer)feature-subset-vector ← best-feature ∪ feature-subset-vectorlast-eer ← Min (vector-eer)end101112131415We found that creating a different feature subset for each user improved the systemrecognition performance. We therefore applied the same greedy search algorithm to find