Editing Machine learning (section)

=== Belief functions ===
{{Main|Dempster–Shafer theory}}
The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as [[probability]], [[Possibility theory|possibility]] and  [[Imprecise probability|imprecise probability theories]]. These theoretical frameworks can be thought of as a kind of learner and have some analogous properties of how evidence is combined (e.g.,  Dempster's rule of combination), just like how in a [[Probability mass function|pmf]]-based Bayesian approach would combine probabilities.<ref>{{Cite journal |last1=Verbert |first1=K. |last2=Babuška |first2=R. |last3=De Schutter |first3=B. |date=2017-04-01 |title=Bayesian and Dempster–Shafer reasoning for knowledge-based fault diagnosis–A comparative study |url=https://www.sciencedirect.com/science/article/abs/pii/S0952197617300118 |journal=Engineering Applications of Artificial Intelligence |volume=60 |pages=136–150 |doi=10.1016/j.engappai.2017.01.011 |issn=0952-1976}}</ref> However, there are many caveats to these beliefs functions when compared to Bayesian approaches in order to incorporate ignorance and [[uncertainty quantification]]. These belief function approaches that are implemented within the machine learning domain typically leverage a fusion approach of various [[ensemble methods]] to better handle the learner's [[decision boundary]], low samples, and ambiguous class issues that standard machine learning approach tend to have difficulty resolving.<ref name="YoosefzadehNajafabadi-2021" /><ref name="Kohavi" /> However, the computational complexity of these algorithms are dependent on the number of propositions (classes), and can lead to a much higher computation time when compared to other machine learning approaches.