Enhancing the generalization of feature construction using genetic programming for imbalanced data with augmented non-overlap degree
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Li:2021:BIBM,
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author = "Zhuang Li and Jingyan Qin and Haiyan Gong and
Xiaotong Zhang and Yadong Wan",
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title = "Enhancing the generalization of feature construction
using genetic programming for imbalanced data with
augmented non-overlap degree",
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booktitle = "2021 IEEE International Conference on Bioinformatics
and Biomedicine (BIBM)",
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year = "2021",
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pages = "960--965",
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month = dec,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/BIBM52615.2021.9669863",
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abstract = "Genetic programming (GP) has a significant achievement
in feature construction and non-overlap degree can help
to improve the generalization ability of GP based
feature construction. However, the non-overlap degree
is biased towards the majority class. In this paper, a
novel GP based feature construction method with
augmented non-overlap degree is proposed to enhance the
generalization ability for imbalanced data. And the
constructed features are evaluated by a novel function
based on the combination of the area under the ROC
curve metric and the augmented non-overlap degree. The
generalization performance is evaluated not only by a
particular classification algorithm, but also by six
widely used classification algorithms. The experiments
conducted on five imbalanced biomedical datasets with
different imbalance rates show that the proposed
GP-AANO method can achieve superior generalization
performance for classification.",
-
notes = "Also known as \cite{9669863}",
- }
Genetic Programming entries for
Zhuang Li
Jingyan Qin
Haiyan Gong
Xiaotong Zhang
Yadong Wan
Citations