Explainable AI: Machine Learning Interpretation in Blackcurrant Powders
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View abstract on PubMed
Summary
This summary is machine-generated.Explainable AI (XAI) enhances understanding of artificial intelligence decisions. This study used XAI models like Decision Tree and Random Forest to accurately identify currant powders based on texture, achieving over 96% performance.
Area Of Science
- Artificial Intelligence
- Machine Learning
- Data Science
Background
- Explainability in machine and deep learning is crucial due to the increasing use of AI.
- Explainable AI (XAI) improves transparency and effectiveness of AI model decisions.
- XAI aids in data mining, error elimination, and enhancing AI algorithm performance.
Purpose Of The Study
- To understand the identification of selected currant powder types using 'glass box' and 'black box' AI models.
- To evaluate the performance of AI models in classifying currant powders based on texture descriptors.
- To visualize model explanations using Local Interpretable Model Agnostic Explanations (LIMEs).
Main Methods
- Utilized Decision Tree and Random Forest models for currant powder identification.
- Trained models using texture descriptors: entropy, contrast, correlation, dissimilarity, and homogeneity.
- Assessed model performance using accuracy, precision, recall, and F1-score metrics.
- Employed Local Interpretable Model Agnostic Explanations (LIMEs) for visualization.
Main Results
- Bagging (Bagging_100), Decision Tree (DT0), and Random Forest (RF7_gini) were the most effective models.
- Bagging_100 achieved approximately 0.979 for accuracy, precision, recall, and F1-score.
- DT0 and RF7_gini models demonstrated classifier performance measures exceeding 96%.
Conclusions
- XAI models, particularly Bagging, Decision Tree, and Random Forest, are effective for identifying currant powders.
- The study highlights the potential of XAI in analyzing food product data.
- Agnostic XAI models can serve as valuable tools for online data analysis in the future.
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