Fitness Functions in Genetic Programming for Classification with Unbalanced Data

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{DBLP:conf/ausai/PattersonZ07,

• author = "Grant Patterson and Mengjie Zhang",
• title = "Fitness Functions in Genetic Programming for Classification with Unbalanced Data",
• booktitle = "Proceedings of the 20th Australian Joint Conference on Artificial Intelligence",
• year = "2007",
• editor = "Mehmet A. Orgun and John Thornton",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• pages = "769--775",
• address = "Gold Coast, Australia",
• month = dec # " 2-6",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-76926-2",
• DOI = "doi:10.1007/978-3-540-76928-6_90",
• size = "11 pages",
• abstract = "This paper describes a genetic programming (GP) approach to binary classification with class imbalance problems. This approach is examined on two benchmark and two synthetic data sets. The results show that when using the overall classification accuracy as the fitness function, the GP system is strongly biased toward the majority class. Two new fitness functions are developed to deal with the class imbalance problem. The experimental results show that both of them substantially improve the performance for the minority class, and the performance for the majority and minority classes is much more balanced.",
• notes = "Pima",

}

Genetic Programming entries for Grant Patterson Mengjie Zhang

Citations