Optimal Pattern Detection Tree for Symbolic Rule-Based Classification

TL;DR

This paper presents the Optimal Pattern Detection Tree (OPDT), a novel rule-based machine learning model using mixed-integer programming to discover optimal, interpretable patterns in data for binary classification tasks.

Contribution

The paper introduces OPDT and BSC framework, enabling optimal pattern discovery with domain knowledge integration, offering transparency and guarantees on pattern optimality.

Findings

OPDT discovers patterns with optimality guarantees.

OPDT performs efficiently on moderately sized datasets.

The BSC framework allows encoding of domain constraints.

Abstract

Pattern discovery in data plays a crucial role across diverse domains, including healthcare, risk assessment, and machinery maintenance. In contrast to black-box deep learning models, symbolic rule discovery emerges as a key data mining task, generating human-interpretable rules that offer both transparency and intuitive explainability. This paper introduces the Optimal Pattern Detection Tree (OPDT), a rule-based machine learning model based on novel mixed-integer programming to discover a single optimal pattern in data through binary classification. To incorporate prior knowledge and compliance requirements, we further introduce the Branching Structure Constraints (BSC) framework, which enables decision makers to encode domain knowledge and constraints directly into the model. This optimization-based approach discovers a hidden underlying pattern in datasets, when it exists, by identifying an optimal rule that maximizes coverage while minimizing the false positive rate due to misclassification. Our computational experiments show that OPDT discovers a pattern with optimality guarantees on moderately sized datasets within reasonable runtime.