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Decoding Natural Behavior from Neuroethological Embedding

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Pseudo datasets estimate feature attribution in artificial neural networks.

Hui-Yi Yang¹, Yi-Hau Chen², Hao-Min Cheng^3,4,5,6

¹Division of Biostatistics and Data Science, Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.

Scientific Reports

|November 22, 2025

Summary

This study introduces Pseudo Datasets Perturbation Effect (PDPE), a new method for explaining neural network predictions. PDPE effectively identifies feature interactions and individual feature importance, offering faster and more accurate insights than existing techniques.

Keywords:

Explainable artificial intelligence Feature attribution Interaction effect Neural network Perturbation-based methods

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Neural networks excel at classification but lack interpretability, hindering their application.
Current explanation methods often overlook feature interactions, limiting a full understanding of model behavior.

Purpose of the Study:

To propose a novel two-stage explanation method, Pseudo Datasets Perturbation Effect (PDPE), for neural networks.
To address the limitations of existing methods by accounting for feature interactions and individual feature significance.

Main Methods:

PDPE perturbs data to observe the impact on predictions, discerning feature importance and interactions.
Evaluates individual features and their interaction terms within structured data.

Main Results:

Computer simulations show PDPE is faster and more accurate than SHAP Value for explaining neural networks.
Real-life data analysis from the National Institute of Diabetes and Digestive and Kidney Diseases confirms PDPE's superior performance.

Conclusions:

PDPE enhances understanding of how individual features and their interactions influence neural network predictions.
The method offers a more comprehensive and efficient approach to model interpretability in machine learning.