Beam search for feature selection in automatic SVM defect classification

Often in pattern classification problems, one tries to extract a large number of features and base the classifier decision on as much information as possible. This yields an array of features that are ‘potentially’ useful. Most of the time however, large feature sets are sub-optimal in describing the samples since they tend to over-represent the data and model noise along with the useful information in the data. Selecting relevant features from the available set of features is, therefore, a challenging task. In this paper, we present an innovative feature selection algorithm called Smart Beam Search (SBS), which is used with a Support Vector Machine based classifier for automatic defect classification. This feature selection approach not only reduces the dimensionality of the feature space substantially, but also improves the classifier performance.