A One-Vs-One Approach to Improve Tangled Program Graph Performance on Classification Tasks
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{DBLP:conf/ijcci/BellangerBCH23,
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author = "Thibaut Bellanger and Matthieu {Le Berre} and
Manuel Clergue and Jin-Kao Hao",
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title = "A One-Vs-One Approach to Improve Tangled Program Graph
Performance on Classification Tasks",
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booktitle = "Proceedings of the 15th International Joint Conference
on Computational Intelligence, IJCCI 2023",
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year = "2023",
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editor = "Niki {van Stein} and Francesco Marcelloni and
H. K. Lam and Marie Cottrell and Joaquim Filipe",
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address = "Rome, Italy",
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publisher = "SCITEPRESS",
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month = nov # " 13-15",
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pages = "53--63",
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keywords = "genetic algorithms, genetic programming,
Classification, Tangled Program Graph, Ensemble
Learning, Evolutionary Machine Learning, Evolutionary
Search and Meta-Heuristics",
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timestamp = "Fri, 08 Dec 2023 12:42:26 +0100",
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biburl = "https://dblp.org/rec/conf/ijcci/BellangerBCH23.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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URL = "https://www.insticc.org/node/TechnicalProgram/ijcci/2023/presentationDetails/121677",
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DOI = "doi:10.5220/0012167700003595",
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abstract = "We propose an approach to improve the classification
performance of the Tangled Programs Graph (TPG). TPG is
a genetic programming method that aims to discover
Directed Acyclic Graphs (DAGs) through an evolutionary
process, where the edges carry programs that allow
nodes to create a route from the root to a leaf, and
the leaves represent actions or labels in
classification. Despite notable successes in
reinforcement learning tasks, TPG performance in
classification appears to be limited in its basic
version, as evidenced by the scores obtained on the
MNIST dataset. However, the advantage of TPG compared
to neural networks is to obtain, like decision trees, a
global decision that is decomposable into simple atomic
decisions and thus more easily explainable. Compared to
decision trees, TPG has the advantage that atomic
decisions benefit from the expressiveness of a pseudo
register-based programming language, and the graph
evolutionary construction prevents the emergence of
overfitting. Our approach consists of decomposing the
multi-class problem into a set of one-vs-one binary
problems, training a set of TPG for each of them, and
then combining the results of the TPGs to obtain a
global decision, after selecting the best ones by a
genetic algorithm. We test our approach on several
benchmark datasets, and the results obtained are
promising and tend to validate the proposed method.",
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notes = "ECTA23-RP-32",
- }
Genetic Programming entries for
Thibaut Bellanger
Matthieu Le Berre
Manuel Clergue
Jin-Kao Hao
Citations