In this section
@inproceedings{Potyka:2023:10.1609/aaai.v37i8.26132,
author = {Potyka, N and Yin, X and Toni, F},
doi = {10.1609/aaai.v37i8.26132},
pages = {9458--9460},
title = {Explaining random forests using bipolar argumentation and Markov networks},
url = {http://dx.doi.org/10.1609/aaai.v37i8.26132},
year = {2023}
}
TY - CPAPER
AB - Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems. However, as the number of trees and their individual size can be large, their decision making process is often incomprehensible. We show that their decision process can be naturally represented as an argumentation problem, which allows creating global explanations via argumentative reasoning. We generalize sufficientand necessary argumentative explanations using a Markov network encoding, discuss the relevance of these explanations and establish relationships to families of abductive explanations from the literature. As the complexity of the explanation problems is high, we present an efficient approximation algorithm with probabilistic approximation guarantees.
AU - Potyka,N
AU - Yin,X
AU - Toni,F
DO - 10.1609/aaai.v37i8.26132
EP - 9460
PY - 2023///
SN - 2159-5399
SP - 9458
TI - Explaining random forests using bipolar argumentation and Markov networks
UR - http://dx.doi.org/10.1609/aaai.v37i8.26132
UR - http://hdl.handle.net/10044/1/101901
ER -
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