What's inside the black-box?: a genetic programming method for interpreting complex machine learning models
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Evans:2019:GECCO,
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author = "Benjamin P. Evans and Bing Xue and Mengjie Zhang",
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title = "What's inside the black-box?: a genetic programming
method for interpreting complex machine learning
models",
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booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2019",
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editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and
Anne Auger and Petr Posik and Leslie {Peprez Caceres} and
Andrew M. Sutton and Nadarajen Veerapen and
Christine Solnon and Andries Engelbrecht and Stephane Doncieux and
Sebastian Risi and Penousal Machado and
Vanessa Volz and Christian Blum and Francisco Chicano and
Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and
Jonathan Fieldsend and Jose Antonio Lozano and
Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and
Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and
Robin Purshouse and Thomas Baeck and Justyna Petke and
Giuliano Antoniol and Johannes Lengler and
Per Kristian Lehre",
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isbn13 = "978-1-4503-6111-8",
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pages = "1012--1020",
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address = "Prague, Czech Republic",
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DOI = "doi:10.1145/3321707.3321726",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "13-17 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, Explainable
Artificial Intelligence, Interpretable Machine
Learning, Evolutionary Multi-objective Optimisation",
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size = "9 pages",
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abstract = "Interpreting state-of-the-art machine learning
algorithms can be difficult. For example, why does a
complex ensemble predict a particular class? Existing
approaches to interpretable machine learning tend to be
either local in their explanations, apply only to a
particular algorithm, or overly complex in their global
explanations. In this work, we propose a global model
extraction method which uses multi-objective genetic
programming to construct accurate, simplistic and
model-agnostic representations of complex black-box
estimators. We found the resulting representations are
far simpler than existing approaches while providing
comparable reconstructive performance. This is
demonstrated on a range of datasets, by approximating
the knowledge of complex black-box models such as 200
layer neural networks and ensembles of 500 trees, with
a single tree.",
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notes = "Also known as \cite{3321726} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Benjamin Evans
Bing Xue
Mengjie Zhang
Citations