Hybrid Ensemble optimized algorithm based on Genetic Programming for imbalanced data classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Misc{DBLP:journals/corr/abs-2106-01176,
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author = "Maliheh Roknizadeh and Hossein Monshizadeh Naeen",
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title = "Hybrid Ensemble optimized algorithm based on Genetic
Programming for imbalanced data classification",
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howpublished = "arXiv",
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volume = "abs/2106.01176",
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year = "2021",
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month = "2 " # jun,
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keywords = "genetic algorithms, genetic programming, Bagging,
Boosting, Hybrid ensemble method, Genetic programming
algorithm, combination classifier, SMOTE",
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URL = "https://arxiv.org/abs/2106.01176",
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eprinttype = "arXiv",
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eprint = "2106.01176",
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timestamp = "Thu, 10 Jun 2021 01:00:00 +0200",
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biburl = "https://dblp.org/rec/journals/corr/abs-2106-01176.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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size = "11 pages",
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abstract = "One of the most significant current discussions in the
field of data mining is classifying imbalanced data. In
recent years, several ways are proposed such as
algorithm level (internal) approaches, data level
(external) techniques, and cost-sensitive methods.
Although extensive research has been carried out on
imbalanced data classification, however, several
unsolved challenges remain such as no attention to the
importance of samples to balance, determine the
appropriate number of classifiers, and no optimization
of classifiers in the combination of classifiers. The
purpose of this paper is to improve the efficiency of
the ensemble method in the sampling of training data
sets, especially in the minority class, and to
determine better basic classifiers for combining
classifiers than existing methods. We proposed a hybrid
ensemble algorithm based on Genetic Programming (GP)
for two classes of imbalanced data classification. In
this study uses historical data from UCI Machine
Learning Repository to assess minority classes in
imbalanced datasets. The performance of our proposed
algorithm is evaluated by Rapid-miner studio v.7.5.
Experimental results show the performance of the
proposed method on the specified data sets in the size
of the training set shows 40percent and 50percent
better accuracy than other dimensions of the minority
class prediction.",
- }
Genetic Programming entries for
Maliheh Roknizadeh
Hossein Monshizadeh Naeen
Citations