Detecting Overlapping Areas in Unbalanced High-dimensional Data Using Neighborhood Rough Set and Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Pei:ieeeTEC,
-
author = "Wenbin Pei and Bing Xue and Lin Shang and
Mengjie Zhang",
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title = "Detecting Overlapping Areas in Unbalanced
High-dimensional Data Using Neighborhood Rough Set and
Genetic Programming",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2023",
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volume = "27",
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number = "4",
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pages = "1130--1144",
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month = aug,
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2022.3203862",
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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