Indecision trees: learning argument-based reasoning under quantified uncertainty

Jonathan S. Kent; David H. Ménager

doi:10.1117/12.2652598

13 June 2023 Indecision trees: learning argument-based reasoning under quantified uncertainty

Jonathan S. Kent, David H. Ménager

Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 1252915 (2023) https://doi.org/10.1117/12.2652598
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

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 and David H. Ménager "Indecision trees: learning argument-based reasoning under quantified uncertainty", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 1252915 (13 June 2023); https://doi.org/10.1117/12.2652598

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