Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866908552801550336 |
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| author | Murthy, Akshay Sebastian, Shawn Shangle, Manil Wang, Huaduo Dasgupta, Sopam Gupta, Gopal |
| author_facet | Murthy, Akshay Sebastian, Shawn Shangle, Manil Wang, Huaduo Dasgupta, Sopam Gupta, Gopal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18139 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification Murthy, Akshay Sebastian, Shawn Shangle, Manil Wang, Huaduo Dasgupta, Sopam Gupta, Gopal Machine Learning 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. |
| title | Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.18139 |