Models with transparent inner structure and high classification performance
are required to reduce potential risk and provide trust for users in domains
like health care, finance, security, etc. However, existing models are hard to
simultaneously satisfy the above two properties. In this paper, we propose a
new hierarchical rule-based model for classification tasks, named Concept Rule
Sets (CRS), which has both a strong expressive ability and a transparent inner
structure. To address the challenge of efficiently learning the
non-differentiable CRS model, we propose a novel neural network architecture,
Multilayer Logical Perceptron (MLLP), which is a continuous version of CRS.
Using MLLP and the Random Binarization (RB) method we proposed, we can search
the discrete solution of CRS in continuous space using gradient descent and
ensure the discrete CRS acts almost the same as the corresponding continuous
MLLP. Experiments on 12 public data sets show that CRS outperforms the
state-of-the-art approaches and the complexity of the learned CRS is close to
the simple decision tree. Source code is available at
this https URL

本论文提出了一种基于概念规则集的分层规则模型，同时提出了一种新的神经网络模型和方法以便高效地学习该模型，并证明了在 12 个公共数据集上该模型能够胜过先前的模型，并具有接近简单决策树的复杂度。