| Abstract |
Policy-based ensembles have been proposed as a method for combining model predictions in a coalition envi- ronment. Combination policies can be defined so that model predictions are weighted by the uncertainty of the prediction. This paper builds upon the recent #predict mode of a symbolic learner, called FastLAS, and defines a notion of uncertainty of FastLAS predictions using subjective logic. This notion enables learned FastLAS models to be used within a hybrid policy-based ensemble alongside other machine learning models. |
| Authors |
- Daniel Cunnington (IBM UK)
- Yaniv Aspis (Imperial)
- Mark Law (Imperial)
- Alessandra Russo (Imperial)
- Krysia Broda (Imperial)
- Jorge Lobo (Imperial)
- Elisa Bertino (Purdue)
- Dinesh Verma (IBM US)
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| Date |
Sep-2020 |
| Venue |
4th Annual Fall Meeting of the DAIS ITA, 2020 |
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