Transparent Classification with Multilayer Logical Perceptrons and Random Binarization (1912.04695v2)

Abstract: 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 https://github.com/12wang3/mllp.

• The paper presents the Concept Rule Sets (CRS) that combine transparency with expressivity for interpretable AI.
• It introduces a differentiable Multilayer Logical Perceptron (MLLP) with Random Binarization to bridge continuous optimization and discrete rule extraction.
• Empirical results on 12 datasets show that the model outperforms traditional and ensemble methods while maintaining decision tree-like simplicity.

Overview of Transparent Classification with Multilayer Logical Perceptrons and Random Binarization

The paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization" addresses the critical need for both high performance and interpretability in machine learning models, particularly in domains like healthcare, finance, and security where trust and transparency are paramount. The authors propose a novel hierarchical rule-based model termed Concept Rule Sets (CRS), which aims to bridge the gap between expressive power and transparent internal structure, a challenge prevalent in existing models.

The principal innovation lies in the development of the Multilayer Logical Perceptron (MLLP), a continuous approximation of CRS, enabling the application of gradient descent for efficient learning. This approach is supported by the Random Binarization (RB) method, which assists in maintaining the behavioral consistency between the discrete CRS and the continuous MLLP. The empirical evaluation on 12 public datasets demonstrates that CRS not only outperforms state-of-the-art methods in terms of classification performance but also maintains a complexity akin to that of simple decision trees.

Key Contributions

The paper's contributions are outlined as follows:

1. Concept Rule Sets (CRS): CRS is a hierarchical rule-based model designed to offer transparency and expressivity concurrently. Each node within CSR represents a specific rule or rule set, facilitating intuitive interpretation.
2. Multilayer Logical Perceptron (MLLP): MLLP serves as a differentiable counterpart to CRS, supporting the training of rule-based models using gradient descent. The use of logical activation functions and constrained weights allows MLLP to mirror the behaviors of rule-based models faithfully.
3. Random Binarization (RB) Method: To ensure alignment between the discrete CRS and the continuous MLLP, the RB method is employed. This involves binarizing randomly selected weights during training, thus enhancing the convergence and effective extraction of CRS from MLLP.
4. Model Simplification Techniques: The authors introduce methods for simplifying CRS by detecting dead nodes and eliminating redundant rules, thereby optimizing model complexity without compromising performance.

Numerical Results and Implications

The evaluation across multiple datasets reveals several significant observations:

• CRS achieves higher average F1 scores compared to traditional models like C4.5, CART, and even complex ensemble models like Random Forest (with 100 estimators), suggesting its potent classification capability.
• The RB method is shown to be crucial for ensuring that MLLP trained parameters translate effectively into high-performing discrete CRS models.
• Moreover, CRS shows a complexity close to decision trees while offering substantial improvements in interpretability and performance, aligning well with the contemporary demand for transparent AI systems.

Implications for AI Developments

From a practical perspective, CRS and its underlying methodologies offer a promising direction for creating interpretable AI models that do not sacrifice predictive power. These models are particularly relevant for applications where decision rationale needs to be clearly articulated, such as in legal or medical settings. Theoretically, the introduction of logical activation functions and RB methods could inspire new strategies for bridging the gap between continuous optimization techniques and discrete model representations in machine learning.

Future Directions

While the current work effectively addresses transparency and performance, future research could focus on broadening the applicability of CRS by reducing reliance on data discretization processes, which can introduce biases for certain datasets. Additionally, exploring the scalability of CRS for more complex unstructured data could further extend its utility in a broader range of AI applications. As transparency becomes increasingly critical in AI, models like CRS that inherently balance prediction accuracy with interpretability will continue to gain prominence in both academia and industry.