Induction of decision trees as classification models through metaheuristics
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- @Article{RIVERALOPEZ:2022:SEC,
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author = "Rafael Rivera-Lopez and Juana Canul-Reich and
Efren Mezura-Montes and Marco Antonio Cruz-Chavez",
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title = "Induction of decision trees as classification models
through metaheuristics",
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journal = "Swarm and Evolutionary Computation",
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volume = "69",
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pages = "101006",
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year = "2022",
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ISSN = "2210-6502",
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DOI = "doi:10.1016/j.swevo.2021.101006",
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URL = "https://www.sciencedirect.com/science/article/pii/S2210650221001681",
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keywords = "genetic algorithms, genetic programming, Machine
learning, Single-solution-based metaheuristics,
Evolutionary algorithms, Swarm intelligence methods",
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abstract = "The induction of decision trees is a widely-used
approach to build classification models that guarantee
high performance and expressiveness. Since a
recursive-partitioning strategy guided for some
splitting criterion is commonly used to induce these
classifiers, overfitting, attribute selection bias, and
instability to small training set changes are
well-known problems in them. Other approaches, such as
incremental induction, classifier ensembles, and the
global search in the decision-tree-space, have been
implemented to overcome these problems. In particular,
metaheuristics such as simulated annealing, genetic
algorithms, genetic programming, and ant colony
optimization have been used to induce compact and
accurate decision trees. This paper presents a
state-of-the-art review of the use of
single-solution-based metaheuristics and swarm and
evolutionary computation algorithms to build decision
trees as classification models. We outline the
decision-tree-induction process components and detail
the existing literature studies on metaheuristic-based
approaches to building these classifiers. Several
timelines showing the chronological order in which
these approaches were introduced in the literature are
included. A summary analysis of these studies is also
conducted, focusing on their internal components and
experimental studies. This work provides a useful
reference point for future research in this field",
- }
Genetic Programming entries for
Rafael Rivera-Lopez
Juana Canul-Reich
Efren Mezura-Montes
Marco Antonio Cruz-Chavez
Citations