Genetic programming for development of cost-sensitive classifiers for binary high-dimensional unbalanced classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{PEI:2021:ASC,

• author = "Wenbin Pei and Bing Xue and Lin Shang and Mengjie Zhang",
• title = "Genetic programming for development of cost-sensitive classifiers for binary high-dimensional unbalanced classification",
• journal = "Applied Soft Computing",
• volume = "101",
• pages = "106989",
• year = "2021",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2020.106989",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494620309285",
• keywords = "genetic algorithms, genetic programming, Classification, High-dimensionality, Class imbalance",
• abstract = "Genetic programming (GP) has the built-in ability for feature selection when developing classifiers for classification with high-dimensional data. However, due to the problem of class imbalance, the developed classifiers by GP are prone to be biased towards the majority class. Cost-sensitive learning has shown to be effective in addressing the problem of class imbalance. In cost-sensitive learning, cost matrices are often manually designed and then considered by classification algorithms to treat different mistakes differently. However, in many real-world applications, cost matrices are unknown because of the limited domain knowledge in complex situations. Therefore, in this paper, we propose a novel GP method to develop cost-sensitive classifiers, where a cost matrix is automatically learned, instead of requiring it from domain experts. The proposed method is examined and compared with existing methods on ten high-dimensional unbalanced datasets. Experimental results show that the proposed method outperforms the compared GP methods in most cases",

}

Genetic Programming entries for Wenbin Pei Bing Xue Lin Shang Mengjie Zhang

Citations