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arXiv · 2206.12252

Indecision Trees: Learning Argument-Based Reasoning under Quantified Uncertainty

Jonathan S. Kent·David H. Menager
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Abstract

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.

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Jonathan S. Kent, David H. Menager. 2022-06-23. Indecision Trees: Learning Argument-Based Reasoning under Quantified Uncertainty. https://doi.org/10.1117/12.2652598

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