Visual analytics for human-centered machine learning
We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps
between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence …
between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence …
Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority …
G Yao, X Hu, T Zhou, Y Zhang - Expert Systems, 2022 - Wiley Online Library
The spread of enterprise credit risk in the supply chain may lead to large‐scale bankruptcy
and credit crises, which are related to national economic and social stability and financial …
and credit crises, which are related to national economic and social stability and financial …
Re-interpreting rules interpretability
Trustworthy machine learning requires a high level of interpretability of machine learning
models, yet many models are inherently black-boxes. Training interpretable models instead …
models, yet many models are inherently black-boxes. Training interpretable models instead …
Symbolic Data Analysis to Improve Completeness of Model Combination Methods
A growing number of organizations are adopting a strategy of breaking down large data
analysis problems into specific sub-problems, tailoring models for each. However, handling …
analysis problems into specific sub-problems, tailoring models for each. However, handling …
Simplifying Random Forests Through Post-Hoc Rule Extraction
L Brakke - 2024 - search.proquest.com
Despite the high accuracy of black-box models, a significant challenge remains: their
decision-making processes are often too complex for humans to easily understand. In …
decision-making processes are often too complex for humans to easily understand. In …