Interpretability in Multidimensional Classification

Generating rule-based models from data is an efficient way of inferring information from large datasets. In high-dimensional spaces, the complexity of the model itself can undermine the interpretability of this information. This chapter introduces metrics quantifying the information flow between inputs, feature dimensions and output classes. These metrics are used to estimate the contribution of individual input features to a fuzzy classification task without making explicit use of the data underlying the model. Application of these techniques to a speech classification problem shows that significant reduction in the model dimensionality can be achieved with minimal accuracy loss.