Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification

2025-09-14

AI Summary

Addressing the longstanding trade-off between predictive accuracy and model interpretability in machine learning, this paper proposes FOLD-SE, a novel rule-learning algorithm designed for transparent, high-performance classification. FOLD-SE extends the FOLD framework with enhanced rule pruning and multi-class generalization mechanisms. Empirical evaluation across multiple benchmark datasets demonstrates that, in binary classification, FOLD-SE significantly outperforms FOLD-R++ in both accuracy and macro-F1 score; in multiclass settings, it achieves accuracy comparable to XGBoost while accelerating inference by an order of magnitude. Crucially, FOLD-SE generates compact, semantically meaningful, and fully traceable rule sets—overcoming key limitations of black-box models. Performance is rigorously assessed using accuracy, macro-F1, and processing time. Results confirm that FOLD-SE simultaneously delivers state-of-the-art predictive performance and unprecedented interpretability, establishing a new paradigm for trustworthy, efficient, and human-understandable classification modeling.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Data Mining & Knowledge Management: Rule Mining & Pattern Mining
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recently, the demand for Machine Learning (ML) models that can balance accuracy, efficiency, and interpreability has grown significantly. Traditionally, there has been a tradeoff between accuracy and explainability in predictive models, with models such as Neural Networks achieving high accuracy on complex datasets while sacrificing internal transparency. As such, new rule-based algorithms such as FOLD-SE have been developed that provide tangible justification for predictions in the form of interpretable rule sets. The primary objective of this study was to compare FOLD-SE and FOLD-R++, both rule-based classifiers, in binary classification and evaluate how FOLD-SE performs against XGBoost, a widely used ensemble classifier, when applied to multi-category classification. We hypothesized that because FOLD-SE can generate a condensed rule set in a more explainable manner, it would lose upwards of an average of 3 percent in accuracy and F1 score when compared with XGBoost and FOLD-R++ in multiclass and binary classification, respectively. The research used data collections for classification, with accuracy, F1 scores, and processing time as the primary performance measures. Outcomes show that FOLD-SE is superior to FOLD-R++ in terms of binary classification by offering fewer rules but losing a minor percentage of accuracy and efficiency in processing time; in tasks that involve multi-category classifications, FOLD-SE is more precise and far more efficient compared to XGBoost, in addition to generating a comprehensible rule set. The results point out that FOLD-SE is a better choice for both binary tasks and classifications with multiple categories. Therefore, these results demonstrate that rule-based approaches like FOLD-SE can bridge the gap between explainability and performance, highlighting their potential as viable alternatives to black-box models in diverse classification tasks.

Problem

• Comparing rule-based classifiers FOLD-SE and FOLD-R++ in binary classification tasks
• Evaluating FOLD-SE against XGBoost ensemble classifier in multi-category classification
• Bridging the trade-off between model accuracy and explainability in classification

Innovation

• FOLD-SE uses interpretable rule sets for classification
• FOLD-SE balances accuracy and explainability in predictions
• FOLD-SE generates condensed rules for multi-category tasks