Ensembles-based Feature Guided Analysis

2026-03-20

AI Summary

This study addresses the low rule recall inherent in existing Feature-Guided Analysis (FGA) methods for interpreting deep neural networks, which limits their practical applicability. To overcome this limitation, the work introduces ensemble learning into FGA for the first time and proposes a scalable rule aggregation mechanism that combines multiple neuron activation rules according to three distinct criteria. This approach significantly improves recall while maintaining high precision. Experimental results on the MNIST and LSC datasets demonstrate that the proposed method increases training recall by 28.51% and 33.15%, respectively, and boosts test recall by 25.76% and 30.81%, with a negligible drop in test accuracy of less than 1%. These findings illustrate a flexible and effective trade-off between precision and recall in model interpretation.

Technology Category

• Machine Learning: Ensemble Methods
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Recent Deep Neural Networks (DNN) applications ask for techniques that can explain their behavior. Existing solutions, such as Feature Guided Analysis (FGA), extract rules on their internal behaviors, e.g., by providing explanations related to neurons activation. Results from the literature show that these rules have considerable precision (i.e., they correctly predict certain classes of features), but the recall (i.e., the number of situations these rule apply) is more limited. To mitigate this problem, this paper presents Ensembles-based Feature Guided Analysis (EFGA). EFGA combines rules extracted by FGA into ensembles. Ensembles aggregate different rules to increase their applicability depending on an aggregation criterion, a policy that dictates how to combine rules into ensembles. Although our solution is extensible, and different aggregation criteria can be developed by users, in this work, we considered three different aggregation criteria. We evaluated how the choice of the criterion influences the effectiveness of EFGA on two benchmarks (i.e., the MNIST and LSC datasets), and found that different aggregation criteria offer alternative trade-offs between precision and recall. We then compare EFGA with FGA. For this experiment, we selected an aggregation criterion that provides a reasonable trade-off between precision and recall. Our results show that EFGA has higher train recall (+28.51% on MNIST, +33.15% on LSC), and test recall (+25.76% on MNIST, +30.81% on LSC) than FGA, with a negligible reduction on the test precision (-0.89% on MNIST, -0.69% on LSC).

Problem

• Feature Guided Analysis
• Deep Neural Networks
• Explainability
• Rule Extraction
• Recall

Innovation

• Ensembles-based Feature Guided Analysis
• Rule Aggregation
• Explainable AI
• Recall-Precision Trade-off
• Deep Neural Network Interpretability