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:1.8051

@InProceedings{Li:2021:BIBM,

• author = "Zhuang Li and Jingyan Qin and Haiyan Gong and Xiaotong Zhang and Yadong Wan",
• title = "Enhancing the generalization of feature construction using genetic programming for imbalanced data with augmented non-overlap degree",
• booktitle = "2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
• year = "2021",
• pages = "960--965",
• month = dec,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/BIBM52615.2021.9669863",
• 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