.png){kind=icon}
Return
Statistically testing training data for unwanted error patterns using rule-oriented regression
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2025.130156.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• A method to detect biases in training data and de-poison it before training. • Examples of discovering biases and patterns in existing data sets. • Explainable AI via fuzzy reasoning combined with classical regression. • Using Boolean formulae to explain data, with statistical significance testing. • An open-source prototype implementation.
Keywords:
Regression analysis
Fuzzy logic
Data bias
Explainable artificial intelligence
XAI
AI
artificial intelligence
CART
classification and regression trees
LASSO
least absolute shrinkage and selection operator
XAI
explainable AI
CSV
comma separated values
GPA
grade point average
FAIR
findability, accessibility, interoperability, and reuse of digital assets
WM
Wang-Mendel
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
Organization
Cited Papers
No cited papers available