Research to Revenue
A comparative case study on the performance of global sensitivity analysis methods on digit classification
This article explores the effectiveness of various Global Sensitivity Analysis (GSA) methods in identifying influential features for deep learning models, specifically in digit classification tasks. It highlights how different mathematical foundations of GSA can lead to varying conclusions about feature importance and proposes a methodology to evaluate their efficacy.
Executive Impact & Key Metrics
This research provides a critical evaluation of Global Sensitivity Analysis (GSA) methods for deep learning in digit classification. By systematically comparing Sobol, FAST, Morris, and Delta, we identify the most effective approaches for feature selection, enhancing model explainability and potentially reducing computational burden. Our findings guide enterprises in selecting optimal GSA techniques to interpret complex AI models, leading to more robust and transparent AI deployments.
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Reduction in Feature Space
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Improvement in Model Explainability
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Operational Efficiency Gain
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
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Classification Accuracy Achieved on MNIST Data
Enterprise Process Flow
Train CNN Model (MNIST)
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Apply SA Methods (Test Data)
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Identify Important Features
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Sort & Segment Pixels
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Evaluate Segment Impact
Sobol & FAST: Uncovering Key Pixels
The study found that Sobol's total-order index (ST) and FAST S1 demonstrated superior performance in identifying influential pixels for digit classification. These methods effectively capture both individual and interactive effects of features, leading to higher classification accuracies when using subsets of important pixels. This makes them highly suitable for feature selection in high-dimensional visual data.
- Identified influential pixels for digit classification.
- Suitable for high-dimensional visual data.
- Captures individual and interactive feature effects.
| Method | Strengths | Weaknesses |
|---|---|---|
| Sobol |
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| FAST |
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Morris Method: Efficient Feature Screening
The Morris method (μ* and σ indices) proved highly efficient in screening features due to its lower computational cost compared to variance-based methods. It effectively identifies parameters with significant individual influence and those involved in interactions or non-linear effects, offering a good balance between speed and interpretability. This makes it valuable for initial feature reduction in large datasets.
- Efficient for feature screening.
- Lower computational cost.
- Identifies individual and interactive/non-linear effects.
| Method | Strengths | Weaknesses |
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| Morris |
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| DGSM |
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Your AI Explainability Roadmap
A phased approach to integrating advanced explainability, tailored for enterprise success.
Phase 1: Discovery & Assessment
Evaluate existing AI models, identify explainability gaps, and define key performance indicators (KPIs) for transparency and interpretability.
Phase 2: Pilot Implementation
Deploy selected GSA methods on a pilot project, analyzing influential features and comparing performance against baselines.
Phase 3: Integration & Optimization
Integrate robust GSA solutions into your enterprise AI pipeline, fine-tuning for efficiency and continuous monitoring of model explainability.
Phase 4: Scaling & Governance
Scale explainability solutions across the organization, establish governance frameworks, and train teams on interpreting AI insights.
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