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2115. A Novel Ensemble Framework for XAI-Based Feature Selection in Machine Learning Models
Invited abstract in session MB-4: Recent Methodologies in Explainable AI (XAI) 2, stream Recent Advancements in AI .
Monday, 10:30-12:00Room: 1001 (building: 202)
Authors (first author is the speaker)
| 1. | Sureyya Ozogur-Akyuz
|
| Department of Mathematics Engineering, Bahcesehir University | |
| 2. | Halil ibrahim Demirel
|
Abstract
This study introduces an innovative ensemble framework that leverages Explainable Artificial Intelligence (XAI) to enhance feature selection processes in machine learning (ML) models. The primary aim is to improve the interpretability and predictive performance of these models. Our approach integrates multiple ensemble learning algorithms, including Gradient Boosting Machines and Random Forests, with XAI techniques like SHAP to create a transparent, interpretable model. We evaluate our framework on various datasets, demonstrating its ability to maintain high predictive accuracy while providing insights into the feature selection process. The results indicate that our ensemble framework significantly enhances model transparency and interpretability without compromising on performance.
Keywords
- Machine Learning
Status: accepted
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