Transformation radial basis neural network for relevant feature selection
Radial basis neural networks are excellent candidates for selecting relevant features in pattern recognition problems. By a slight change in the traditional three-layer architecture of a radial basis neural network, we can obtain a quantitative method, which allows us to get a ranking within the features. We present a new neural network concept, combining at the same time two different skills: classification and detection of relevant features in the input vector.
radial basis neural network relevant feature extraction multidimensional data