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Learning Accurate and Interpretable Decision Rule Sets from Neural Networks


This thesis proposes a novel way to learn a set of the Boolean rules in disjunctive normal form as an interpretable model for binary classifications. We consider the problem of learning an interpretable decision rule set as training a neural network in a specific architecture and converting each neuron in the network into a set of minimal decision rules. This approach can easily find a set of interpretable decision rules that has similarly high predictive performance as a full-precision fully-connected deep neural network, and the method can balance between accuracy and complexity. Moreover, we prove that the set of decision rules derived from each neuron is minimum, unique, and irredundant with respect to the original neuron. Our method is competitive with several other state-of-the-art rule learning algorithms, even with fewer rules and simpler rule conditions

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