High-dimensional Unbalanced Binary Classification by Genetic Programming with Multi-criterion Fitness Evaluation and Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Pei:2022:EC,

• author = "Wenbin Pei and Bing Xue and Lin Shang and Mengjie Zhang",
• title = "High-dimensional Unbalanced Binary Classification by Genetic Programming with Multi-criterion Fitness Evaluation and Selection",
• journal = "Evolutionary Computation",
• year = "2022",
• volume = "30",
• number = "1",
• pages = "99--129",
• month = "Spring",
• keywords = "genetic algorithms, genetic programming, Classification, High dimensionality, Class imbalance",
• ISSN = "1063-6560",
• DOI = "doi:10.1162/evco_a_00304",
• size = "31 pages",
• abstract = "High-dimensional unbalanced classification is challenging because of the joint effects of high dimensionality and class imbalance. Genetic programming (GP) has the potential benefits for use in high-dimensional classification due to its built-in capability to select informative features. However, once data is not evenly distributed, GP tends to develop biased classifiers which achieve a high accuracy on the majority class but a low accuracy on the minority class. Unfortunately, the minority class is often at least as important as the majority class. It is of importance to investigate how GP can be effectively used for high-dimensional unbalanced classification. to address the performance bias issue of GP, a new two-criterion fitness function is developed, which considers two criteria, i.e. the approximation of area under the curve (AUC) and the classification clarity (i.e. how well a program can separate two classes). The obtained values on the two criteria are combined in pairs, instead of summing them together. Furthermore, this paper designs a three-criterion tournament selection to effectively identify and select good programs to be used by genetic operators for generating better offspring during the evolutionary learning process. The experimental results show that the proposed method achieves better classification performance than other compared methods.",

}

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

Citations