Applying Genetic Programming to Improve Interpretability in Machine Learning Models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Ferreira:2020:CEC,
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author = "Leonardo Augusto Ferreira and
Frederico Gadelha Guimaraes and Rodrigo Silva",
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title = "Applying Genetic Programming to Improve
Interpretability in Machine Learning Models",
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booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC
2020",
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year = "2020",
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editor = "Yaochu Jin",
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pages = "paper id24516",
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address = "internet",
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month = "19-24 " # jul,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, XAI,
Interpretability, Machine Learning, Explainability",
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isbn13 = "978-1-7281-6929-3",
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DOI = "doi:10.1109/CEC48606.2020.9185620",
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size = "8 pages",
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abstract = "Explainable Artificial Intelligence (or xAI) has
become an important research topic in the fields of
Machine Learning and Deep Learning. In this paper, we
propose a Genetic Programming (GP) based approach,
named Genetic Programming Explainer (GPX), to the
problem of explaining decisions computed by AI systems.
The method generates a noise set located in the
neighborhood of the point of interest, whose prediction
should be explained, and fits a local explanation model
for the analyzed sample. The tree structure generated
by GPX provides a comprehensible analytical, possibly
non-linear, symbolic expression which reflects the
local behavior of the complex model. We considered
three machine learning techniques that can be
recognized as complex black-box models: Random Forest,
Deep Neural Network and Support Vector Machine in
twenty data sets for regression and classifications
problems. Our results indicate that the GPX is able to
produce more accurate understanding of complex models
than the state of the art. The results validate the
proposed approach as a novel way to deploy GP to
improve interpretability.",
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notes = "https://wcci2020.org/
Universidade Federal de Minas Gerais, Brazil;
Universidade Federal de Ouro Preto, Brazil.
Also known as \cite{9185620}",
- }
Genetic Programming entries for
Leonardo Augusto Ferreira
Frederico Gadelha Guimaraes
Rodrigo Cesar Pedrosa Silva
Citations