Indecision Trees: Learning Argument-Based Reasoning under Quantified Uncertainty
| Autor: | Jonathan S. Kent, David H. Ménager |
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| Rok vydání: | 2022 |
| Předmět: | |
| DOI: | 10.48550/arxiv.2206.12252 |
| Popis: | Using Machine Learning systems in the real world can often be problematic, with inexplicable black-box models, the assumed certainty of imperfect measurements, or providing a single classification instead of a probability distribution. This paper introduces Indecision Trees, a modification to Decision Trees which learn under uncertainty, can perform inference under uncertainty, provide a robust distribution over the possible labels, and can be disassembled into a set of logical arguments for use in other reasoning systems. Comment: 12 pages, 1 figure |
| Databáze: | OpenAIRE |
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