Tree-Shaped Ensemble of Multi-Label Classifiers using Grammar-Guided Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Moyano:2020:CEC,
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author = "Jose M. Moyano and Eva L. Gibaja and
Krzysztof J. Cios and Sebastian Ventura",
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title = "Tree-Shaped Ensemble of Multi-Label Classifiers using
Grammar-Guided Genetic Programming",
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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 id24049",
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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, Multi-label
classification, Ensemble learning",
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isbn13 = "978-1-7281-6929-3",
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DOI = "doi:10.1109/CEC48606.2020.9185661",
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size = "8 pages",
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abstract = "Multi-label classification paradigm has had a growing
interest because of the emergence of a large number of
classification problems where each of the instances of
the data can be associated with several output labels
simultaneously. Several ensemble methods were proposed
to solve the multilabel classification problem.
However, most of them simply create diversity in the
ensemble by following a random procedure and give the
same importance to all members. In this paper, we
propose a Grammar-Guided Genetic Programming algorithm
to build ensembles of multi-label classifiers. Given a
pool of multilabel classifiers, each of them modeling
dependencies among a subset of k labels, they are
combined into a tree-shaped ensemble. At each node of
the tree, predictions of its children nodes are
combined, while each leaf represents a classifier from
the pool. We propose two configurations for the method:
using a fixed value of k for all classifiers in the
pool, or using a variable value of k for each
classifier, thus being able to capture relationships
among groups of labels of different size in the
ensemble. The experiments performed over sixteen
multi-label dataset and using five evaluation metrics
demonstrated that our method performs significantly
better than the state-of-the-art ensembles of
multilabel classifiers.",
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notes = "G3P-kEMLC, pop=50, 150 gens, crossover & mutation, max
tree size. compare with RAkEL. Holm's stats test. k=6
to k=174
https://wcci2020.org/
University of Cordoba, Spain; Virginia Commonwealth
University, United States of America.
Also known as \cite{9185661}",
- }
Genetic Programming entries for
Jose M Moyano
Eva L Gibaja
Krzysztof J Cios
Sebastian Ventura
Citations