Explaining Symbolic Regression Predictions
Created by W.Langdon from
gpbibliography.bib Revision:1.7538
 @InProceedings{Filho:2020:CEC,

author = "Renato Miranda Filho and Anisio Lacerda and
Gisele L. Pappa",

title = "Explaining Symbolic Regression Predictions",

booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC
2020",

year = "2020",

editor = "Yaochu Jin",

pages = "paper id24598",

address = "internet",

month = "1924 " # jul,

organization = "IEEE Computational Intelligence Society",

publisher = "IEEE Press",

keywords = "genetic algorithms, genetic programming",

isbn13 = "9781728169293",

DOI = "doi:10.1109/CEC48606.2020.9185683",

abstract = "The outgrowing application of machine learning methods
has raised a discussion in the artificial intelligence
community on model transparency. In the center of this
discussion is the question of model explanation and
interpretability. The genetic programming (GP)
community has systematically pointed out as one of the
major advantages of GP the fact that it produces models
that can be interpreted by humans. However, as other
interpretable supervised models, the more complex the
model becomes, the less interpretable it is. This work
focuses on posthoc interpretability of GP for symbolic
regression. This approach does not explain the process
followed by a model to reach a decision. Instead, it
justifies the predictions it makes. The proposed
approach, named Explanation by Local Approximation
(ELA), is simple and model agnostic: it finds the
nearest neighbors of the point we want to explain and
performs a linear regression using this subset of
points. The coefficients of this linear regression are
then used to generate a local explanation to the model.
Results show that the errors of ELA are similar to
those of the regression performed with all points. It
also shows that simple visualizations can provide
insights to the users about the most relevant
attributes.",

notes = "https://wcci2020.org/
UFMG/IFMG, Brazil; UFMG, Brazil.
Also known as \cite{9185683}",
 }
Genetic Programming entries for
Renato Miranda Filho
Anisio Lacerda
Gisele L Pappa
Citations