Detecting Overlapping Areas in Unbalanced High-dimensional Data Using Neighborhood Rough Set and Genetic Programming

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

@Article{Pei:ieeeTEC,

• author = "Wenbin Pei and Bing Xue and Lin Shang and Mengjie Zhang",
• title = "Detecting Overlapping Areas in Unbalanced High-dimensional Data Using Neighborhood Rough Set and Genetic Programming",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2023",
• volume = "27",
• number = "4",
• pages = "1130--1144",
• month = aug,
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1089-778X",
• DOI = "doi:10.1109/TEVC.2022.3203862",
• abstract = "Unbalanced classification has attracted widespread interest because of its broad applications. However, due mainly to the uneven class distribution, constructed classifiers are usually biased towards the majority class, and thereby perform terribly on the minority class. Unfortunately, the minority class is often the class of interest in many real-world applications. High dimensionality often further degrades the classification performance, making it more complicated to address the class imbalance issue. Genetic programming (GP) has been applied to construct classifiers, which can simultaneously select good-quality features to improve the classification performance. To handle the class imbalance issue, cost-sensitive GP classifiers treat the minority class as being more important than the majority class, but this may cause an accuracy decrease in overlapping areas where the prior probabilities of the two classes are almost the same. To date, most cost-sensitive classifica",
• notes = "also known as \cite{9874858}",

}

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

Citations