#shap News & Analysis

5 articles tagged with #shap. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · Mar 46/103

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MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction

Researchers introduce MedFeat, a new AI framework that uses Large Language Models for healthcare feature engineering in clinical tabular predictions. The system incorporates model awareness and domain knowledge to discover clinically meaningful features that outperform traditional approaches and demonstrate robustness across different hospital settings.

AINeutralarXiv – CS AI · Apr 76/10

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Empirical Characterization of Rationale Stability Under Controlled Perturbations for Explainable Pattern Recognition

Researchers propose a new metric to assess consistency of AI model explanations across similar inputs, implementing it on BERT models for sentiment analysis. The framework uses cosine similarity of SHAP values to detect inconsistent reasoning patterns and biased feature reliance, providing more robust evaluation of model behavior.

AIBullisharXiv – CS AI · Mar 36/108

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A Polynomial-Time Axiomatic Alternative to SHAP for Feature Attribution

Researchers have developed ESENSC_rev2, a polynomial-time alternative to SHAP for AI feature attribution that offers similar accuracy with significantly improved computational efficiency. The method uses cooperative game theory and provides theoretical foundations through axiomatic characterization, making it suitable for high-dimensional explainability tasks.

AIBullisharXiv – CS AI · Mar 26/1014

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An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks

Researchers propose an efficient unsupervised federated learning framework for anomaly detection in heterogeneous IoT networks that preserves privacy while leveraging shared features from multiple datasets. The approach uses explainable AI techniques like SHAP for transparency and demonstrates superior performance compared to conventional federated learning methods on real-world IoT datasets.

AINeutralarXiv – CS AI · Mar 175/10

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Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization

Researchers developed a privacy-preserving method using SHAP entropy regularization to protect sensitive user data in explainable AI systems for smart home IoT applications. The approach reduces privacy leakage while maintaining model accuracy and explanation quality.