Intepretable Local Explanations Through Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{andersen:2024:GECCOcomp,
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author = "Hayden Andersen and Andrew Lensen and Will Browne and
Yi Mei",
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title = "Intepretable Local Explanations Through Genetic
Programming",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference Companion",
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year = "2024",
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editor = "Jean-Baptiste Mouret and Kai Qin",
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pages = "247--250",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, explainable
AI, XAI, machine learning, Evolutionary Machine
Learning: Poster",
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isbn13 = "979-8-4007-0495-6",
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DOI = "doi:10.1145/3638530.3654370",
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size = "4 pages",
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abstract = "As machine learning models become increasingly
prevalent in everyday life, there is a growing demand
for explanation of the predictions generated by these
models. However, most models used by companies are
black-boxes in nature, without the capacity to provide
explanations to users. This reduces public trust in
these models, and exists as a barrier to adoption of
machine learning. Research into providing explanations
to users has shown that local explanation techniques
provide more acceptable explanations to users than
attempting to explain an entire model, as a user often
does not need to understand the entirety of a
model.This work builds on prior work in the field to
produce a competitive method for high-fidelity local
explanations using genetic programming. Two different
data representations targeted towards both users with
and without machine learning experience are
evaluated.The experimental results show comparable
fidelity to the state-of-the art, while exhibiting more
comprehensible explanations due to including fewer
features in each explanation. The method enables
decomposable explanations that are easy to interpret,
while still capturing non-linear relationships in the
original model.",
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notes = "GECCO-2024 EML A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Hayden Andersen
Andrew Lensen
Will N Browne
Yi Mei
Citations