Feature extraction by genetic algorithms for neural networks in breast cancer classification
Bahram Ghaffarzadeh Kermani, Matthew White, H. Troy Nagle · 2002
In today's world, in which computerized recognition is expanding its horizons in the field of medicine, breast cancer classification is receiving wide attention. In this application, artificial neural networks have achieved reasonable recognition rates. However, to improve performance, a technique is needed to screen the features of the input data, to extract the important ones and suppress those that are irrelevant. Although neural networks do have this capability to some extent, here it is shown that by using a hybrid genetic algorithm and neural network (GANN), the feature extraction can be performed more effectively. Another advantage of augmenting NN training with a GA is that the extracted features using GA are explicit and perceivable. Although the authors evaluated the technique using breast cancer data, the methodology is designed to handle any other kind of classification task.