title: A methodology to explain neural network classification.
abstract: Neural networks are still frustrating tools in the data mining arsenal. They exhibit excellent modelling performance, but do not give a clue about the structure of their models. We propose a methodology to explain the classification obtained by a multilayer perceptron. We introduce the concept of 'causal importance' and define a saliency measurement allowing the selection of relevant variables. Once the model is trained with the relevant variables only, we define a clustering of the data built from the hidden layer representation. Combining the saliency and the causal importance on a cluster by cluster basis allows an interpretation of the neural network classifier to be built. We illustrate the performances of this methodology on three benchmark datasets.
authors.affiliation: France Télécom R&D, Lannion.
authors.forename: Raphael
authors.initials: R
authors.lastname: Féraud
authors.forename: Fabrice
authors.initials: F
authors.lastname: Clérot
mesh_terms: D002965:Classification / Q000379:methods*; D016000:Cluster Analysis; D016571:Neural Networks, Computer