Enterprise AI Analysis
Pseudo datasets estimate feature attribution in artificial neural networks
Authors: Hui-Yi Yang, Yi-Hau Chen, Hao-Min Cheng & Chao-Yu Guo
Journal: Scientific Reports, Article in Press
Executive Impact
Our novel Pseudo Datasets Perturbation Effect (PDPE) method offers a significant leap in interpreting complex neural network models. By accurately identifying individual feature contributions and critical interaction effects, and outperforming SHAP Value in speed and directional accuracy, PDPE enhances trust and transparency in AI predictions, especially in sensitive domains like healthcare.
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Faster Computation vs. SHAP
0%
Testing Accuracy (Real-World)
YES
Feature Interaction Detection
Keywords: Explainable artificial intelligence, Neural network, Feature attribution, Perturbation-based methods, Interaction effect.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology Development
This research introduces the Pseudo Datasets Perturbation Effect (PDPE), a novel two-stage method for explainable AI. PDPE's perturbation-based approach systematically identifies feature importance and interaction effects in neural networks, addressing key limitations of existing methods like SHAP Value. Its design ensures accurate and efficient explanations for both individual features and their complex interactions, building upon previous work and extending it to dichotomous outcomes and interaction analysis.
Performance Evaluation
Our extensive computer simulation studies rigorously compare PDPE against the widely recognized SHAP Value method within the context of neural networks approximating logistic regression. The results consistently demonstrate that PDPE provides faster and more accurate explanations, particularly in identifying the directional impact of features and detecting interaction effects. Furthermore, real-world data analysis using the National Institute of Diabetes dataset confirms PDPE's superior performance and practical applicability.
Real-World Application
The practical utility of PDPE is showcased through its application to real-life diabetes prediction data from the National Institute of Diabetes and Digestive and Kidney Diseases. The model, explained by PDPE, achieved a testing accuracy of 75.32%, providing transparent insights into critical features like 'Pregnancies*Age' and 'Insulin*Diabetes Pedigree Function'. This demonstrates PDPE's potential to enhance clinical decision-making by offering clear, interpretable explanations for AI predictions in healthcare.
PDPE: A Two-Stage Explanation Method
Optimize Neural Network Parameters
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Generate Pseudo-datasets for Perturbation
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Measure Individual Feature Importance (Main Effect)
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Identify Potential Interaction Terms (Stage 1)
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Incorporate Interactions into Model & Recalculate (Stage 2)
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Derive Final Feature & Interaction Importance
| Feature | PDPE Advantage | SHAP Limitations |
|---|---|---|
| Computational Speed | Significantly faster (e.g., ~15x faster in simulations, see Table 8) | Computationally intensive, especially for complex models or large feature sets |
| Accuracy of Directional Impact | More accurate in identifying positive/negative contributions (aligns with LR coefficients) | Struggles with directional impact, can assign incorrect directions (highlighted in Tables 3, 4, 6, 7) |
| Interaction Detection & Attribution | Explicitly identifies potential interaction terms and quantifies their effects | Fails to account for interactions effectively, leading to inaccurate evaluations |
| Model Agnostic Applicability | Applicable beyond feedforward NNs (RNNs, SVMs, tree-based methods) | Primary methods suited for Feedforward NNs, computationally expensive for general application |
75.32%
Testing Accuracy (Diabetes Prediction)
Applying PDPE to a real-world diabetes dataset, our explained neural network model achieved robust predictive performance, demonstrating its practical efficacy in critical healthcare applications.
Case Study: Transparent Diabetes Prediction
In a real-world application, PDPE was used to explain a neural network model trained on the National Institute of Diabetes and Digestive and Kidney Diseases dataset for diabetes prediction. The model achieved an impressive 75.32% testing accuracy. Crucially, PDPE helped identify key individual features and significant interaction terms, such as 'Pregnancies*Age' and 'Insulin*Diabetes Pedigree Function', that were vital for understanding the model's predictions. This level of transparency is essential for clinicians to make informed judgments and for patients to understand treatment outcomes.
Traditional methods often miss such complex interactions or provide less accurate directional insights. PDPE's ability to elucidate these factors makes it a superior tool for building trust and enabling explainable AI in high-stakes domains like healthcare.
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Your AI Implementation Roadmap
A typical journey to integrate advanced XAI into your enterprise operations.
Phase 01: Strategic Assessment & Data Readiness
Evaluate current AI models, data infrastructure, and specific interpretability requirements. Identify critical 'black-box' systems for PDPE application. This phase includes a detailed audit of data quality and feature engineering pipelines to ensure optimal input for the PDPE method.
Phase 02: PDPE Model Integration & Feature Engineering
Integrate PDPE framework with existing neural networks. Develop and refine pseudo-dataset generation strategies tailored to your enterprise data. Implement the two-stage process for main effect and interaction term identification, focusing on model architecture optimization to approximate desired explainability targets.
Phase 03: Validation, Interpretation & Stakeholder Training
Rigorously validate PDPE explanations against business objectives and domain expertise. Translate technical attribution scores into actionable insights for decision-makers. Conduct workshops and training sessions for data scientists, analysts, and executive stakeholders to foster AI literacy and trust.
Phase 04: Monitoring, Refinement & Scalable Deployment
Establish continuous monitoring of model interpretability and performance. Implement feedback loops for iterative refinement of PDPE parameters. Develop scalable deployment strategies to integrate transparent, explainable AI across enterprise applications, ensuring long-term value and compliance.
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