Extracting -of- Rules from Trained Neural Networks

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Explaining Explanations: An Overview of Interpretability of Machine Learning (2018, neutral): “However, a similar approach proposed by Tsukimoto [29] achieves polynomial-time complexity, and may be more tangible.”
Rule Extraction Algorithm for Deep Neural Networks: A Review (2016, methodology): “Tsukimoto [20] has introduced a rule extraction algorithm that works with different types of neural network, such as recurrent neural network and multilayer perceptron (MLP), whose activation function is monotonic.”
Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach (2006, background): “The linear space spanned by the atoms of Boolean algebra is shown to be a Euclidean space [5].”
Rule extraction from neural networks — A comparative study (2012, methodology): “Tsukimotos method [18] is a decompositional approach that extracts low order rules from each of the node.”
Evolutionary design of neural network tree-integration of decision tree, neural network and GA (2001, background): “2. Design an NN, and then extract a symbolic representation from the NN [ 4 ]-[6].”
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning (2018, unknown): “However, a similar approach proposed by Tsukimoto [29] achieves polynomial-time complexity, and may be more tangible.”