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CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks
Kaizheng Wang1, Keivan Shariatmadar2, Shireen Kudukkil Manchingal3
1DistriNet, Department of Computer Science, Campus Bruges, KU Leuven, Bruges, 8200, Belgium; Flanders Make@KU Leuven, Leuven, Belgium.
Summary
Credal-Set Interval Neural Networks (CreINNs) offer reliable uncertainty estimation for neural networks. This novel approach predicts probability bounds, enhancing classification accuracy and reducing computational load compared to existing methods.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Deep Learning
Background:
- Reliable uncertainty estimation is crucial for enhancing neural network dependability.
- Traditional Interval Neural Networks capture weight uncertainty using deterministic intervals.
Purpose of the Study:
- Introduce Credal-Set Interval Neural Networks (CreINNs) for classification tasks.
- Develop a method for predicting probability intervals to estimate various uncertainties.
Main Methods:
- CreINNs extend Interval Neural Networks by predicting upper and lower probability bounds for each class.
- These probability intervals define a credal set for uncertainty quantification.
- The approach was tested on multiclass and binary classification tasks.
Main Results:
- CreINNs demonstrate superior or comparable uncertainty estimation quality against variational Bayesian Neural Networks (BNNs) and Deep Ensembles.
- Significant reduction in computational complexity during inference compared to variational BNNs.
- Effective uncertainty quantification was confirmed even with interval input data.
Conclusions:
- CreINNs provide an effective method for uncertainty estimation in neural network classification.
- The approach offers a computationally efficient alternative to existing methods.
- CreINNs show promise for applications requiring robust uncertainty quantification, including those with interval data.
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