The role of feature selection in building pattern recognizers for computer aided diagnosis

In this paper we explore the use of feature selection techniques to improve the generalization performance of pattern recognizers for computer aided diagnosis CAD We apply a modi ed version of the sequential forward oating selection SFFS of Pudil et al to the problem of selecting an optimal feature subset for mass detection in digitized mammograms The complete feature set consists of multi scale tangential and radial gradients in the mammogram region of interest We train a simple multi layer perceptron MLP using the SFFS algorithm and compare its performance using a jackknife procedure to an MLP trained on the complete feature set features Results indicate that a variable number of features is chosen in each of the jackknife sets and the test performance Az using the chosen feature subset is no better than the performance using the entire feature set These results may be attributed to the fact that the feature set is noisy and the data set used for training testing is small We next modify the feature selection technique by using the results of the jackknife to compute the frequency at which di erent features are selected We construct a classi er by choosing the top N features selected most frequently which maximize performance on the training data We nd that by adding this hand tuning component to the feature selection process we can reduce the feature set from to features and at the same time have a statistically signi cant increase in generalization performance p