Canonical decision model construction by extracting the mapping function from trained neural networks

This work proposes a decision model construction process by extracting the mapping function from the trained neural model. The construction process contains three tasks, namely, data preprocessing, hyperplane extraction, and decision model translation. The data preprocessing uses the correlation coefficient and canonical analysis for projecting the input vector into the canonical feature space. The hyperplane extraction uses the canonical feature space to train the neural networks and extracts the hyperplanes from the trained neural model. The genetic algorithm is used to adjust the slop and reduce the number of hyperplanes. The decision model translation uses the elliptical canonical model to formulate the preliminary decision model. Finally, the genetic algorithm is used again to optimize the canonical decision model.