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Artificial Neural Networks form the basis of very powerful learning methods.It has been observed that a naive application of fully connected neuralnetworks to data with many irrelevant variables often leads to overfitting. Inan attempt to circumvent this issue, a prior knowledge pertaining to whatfeatures are relevant and their possible feature interactions can be encodedinto these networks. In this work, we use decision trees to capture suchrelevant features and their interactions and define a mapping to encodeextracted relationships into a neural network. This addresses theinitialization related concern of fully connected neural networks. At the sametime through feature selection it enables learning of compact representationscompared to state of the art tree-based approaches. Empirical evaluations andsimulation studies show the superiority of such an approach over fullyconnected neural networks and tree-based approaches

Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks