… Thus, stopping the network at the right time can prevent overfitting from occurring. The
comparisons with LDA and decision tree induction are done using the networks with an optimized …

… extracting Decision Trees from trained Feedforward Neural Networks. We show that the
new method is able to create Decision Trees that are close in accuracy to the Neural Networks …

… of the knowledge stored in neural networks. In this paper, we … of neural networks and the
comprehensibility of decision trees… generating decision trees that utilizes trained neural networks…

… decision tree (DT) baseline we set two of the key hyperparameters criterion as ‘gini’ and splitter
as ‘best’. For neural network … of DNDT, decision tree, and neural network models on each …

… Section III briefly presents background of neural networks and an improvement of … neural
network and its enhancement for new attacks detection. In Section IV, we present decision trees …

… Abstract—In this paper we propose a synergistic melting of neural networks and decision
trees (DT) we call neural decision trees (NDT). NDT is an architecture a la decision tree where …

… The ANN-DT (artificial neural-network decision tree) algorithm described in this paper can
be seen as pedagogical, although it is not bound by the limitations of the above strategies. …

… Then, we explain what kind of neural nets and induction of decision trees were used for …
performance of neural nets is not significant better than the performance of decision trees if we …

… neural network. The objective of this paper is to show how this mapping of decision trees into
a multilayer neural network … class of layered neural networks, called entropy nets, that have …

… mapping from decision trees to deep neural networks. The mapping produces a network
with a … per hidden layer, and a set of initial weights that reflect the decision tree structure. The …