Paper ID: 2206.12252

Indecision Trees: Learning Argument-Based Reasoning under Quantified Uncertainty

Jonathan S. Kent, David H. Menager

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.

Submitted: Jun 23, 2022