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.
翻译:本文提出一种新的模式,用于学习一套独立的逻辑规则,以脱离性正常形式,作为可解释的分类模式。我们认为,学习一个可解释的决定规则的问题,是将神经网络训练在一个具体、但非常简单的两层结构中。第一层的每个神经元直接绘制可解释的地图,在培训后进行当时的规则,第二层的输出神经元直接绘制与第一层规则的脱钩,以形成决定规则集。我们在第一个规则层中代表神经元,使我们能够将决定规则中各特征的正和负关联混为一谈。可以利用国家神经网络培训方法来学习高度精确的分类模型。此外,我们提出了一种基于宽度的正规化方法,以平衡分类准确性和衍生规则的简单性。我们的实验结果表明,我们的方法可以比其他最先进的、准确性的规则学习算法产生更准确性交易。此外,与不易解释的黑箱机器学习方法相比,我们随机的森林和完全的预测性决定可以找到可比较的深度的网络。