Learning Interpretable Rules for Scalable Data Representation and Classification

• URL: http://arxiv.org/abs/2310.14336v3
• Date: Tue, 30 Jan 2024 03:21:30 GMT
• Title: Learning Interpretable Rules for Scalable Data Representation and Classification
• Authors: Zhuo Wang, Wei Zhang, Ning Liu, Jianyong Wang
• Abstract summary: Rule-based Learner Representation (RRL) learns interpretable non-fuzzy rules for data representation and classification. RRL can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios.
• Score: 11.393431987232425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. A novel design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on ten small and four large data sets show that RRL outperforms the competitive interpretable approaches and can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios. Our code is available at: https://github.com/12wang3/rrl.

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