A Modular Framework for Multi category feature selection in Digital mammography

Many existing researches utilized many different approac hes for recognition in digital mammography using various ANN classifier-modeling techniques. Different types of feature extraction techniques are also used. It has been observed that, beyond a certain point, the inclusion of a dditional features leads to a worse rather than better performance. Moreover , the choice of features to represent the patterns affects several a spects of pattern recognition problem such as accuracy, required learning time and necessary number of samples. A common problem with the multi category feature classification is the conflict between the categories. None of the feasible solutions allow simultaneous optimal solution for all categories. In order to find an optimal solutions the searching space can be divided based on individual category in each sub region and finally merging them through decision spport s ystem. In this paper we propose a canonical GA based modular feature s election approach combined with standard MLP.