  This paper proposes a new paradigm for learning a set of independent logical
rules in disjunctive normal form as an interpretable model for classification.
We consider the problem of learning an interpretable decision rule set as
training a neural network in a specific, yet very simple two-layer
architecture. Each neuron in the first layer directly maps to an interpretable
if-then rule after training, and the output neuron in the second layer directly
maps to a disjunction of the first-layer rules to form the decision rule set.
Our representation of neurons in this first rules layer enables us to encode
both the positive and the negative association of features in a decision rule.
State-of-the-art neural net training approaches can be leveraged for learning
highly accurate classification models. Moreover, we propose a sparsity-based
regularization approach to balance between classification accuracy and the
simplicity of the derived rules. Our experimental results show that our method
can generate more accurate decision rule sets than other state-of-the-art
rule-learning algorithms with better accuracy-simplicity trade-offs. Further,
when compared with uninterpretable black-box machine learning approaches such
as random forests and full-precision deep neural networks, our approach can
easily find interpretable decision rule sets that have comparable predictive
performance.
