y0news
AnalyticsDigestsSourcesTopicsRSSAICrypto

#shap News & Analysis

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

7 articles
AIBullisharXiv – CS AI · Mar 46/103
🧠

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 · May 96/10
🧠

CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors

Researchers introduce CatNet, an algorithm that controls False Discovery Rate (FDR) in LSTM neural networks by combining SHAP feature importance derivatives with a Gaussian Mirror statistical approach. The method addresses overfitting and model interpretability challenges in time-series deep learning through improved feature selection and a novel kernel-based independence measure.

AINeutralarXiv – CS AI · Apr 206/10
🧠

Applied Explainability for Large Language Models: A Comparative Study

Researchers compare three explainability techniques—Integrated Gradients, Attention Rollout, and SHAP—for interpreting LLM decisions on sentiment classification tasks. The study reveals that gradient-based methods offer stability and interpretability, while attention-based approaches are faster but less predictive, highlighting critical trade-offs in choosing explanation methods for transformer models.

AIBullisharXiv – CS AI · Mar 36/108
🧠

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
🧠

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.