Exploiting ensemble diversity for automatic feature extraction

摘要

The present invention proposes an automatic method, based on a neural network ensemble, for extracting multiple, diverse and complementary sets of useful classification features from high-dimensional data. The utility of these diverse representations for an image dataset is demonstrated, showing good classification accuracy and a high degree of dimensionality reduction.