Exploiting ensemble diversity for automatic feature extraction

We present an automatic method, based on a neural network ensemble, for extracting multiple, diverse and complementary sets of useful classification features from high-dimensional data. We demonstrate the utility of these diverse representations for an image dataset, showing good classification accuracy and a high degree of dimensionality reduction. We then outline a number of possible extensions to the project in an evolutionary computation context.