On the Impact of Class Imbalance in GP Streaming Classification with Label Budgets

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

@InProceedings{khanchi:2016:EuroGP,

• author = "Sara Khanchi and Malcolm Iain Heywood and Nur Zincir-Heywood",
• title = "On the Impact of Class Imbalance in GP Streaming Classification with Label Budgets",
• booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming",
• year = "2016",
• month = "30 " # mar # "--1 " # apr,
• editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim",
• series = "LNCS",
• volume = "9594",
• publisher = "Springer Verlag",
• address = "Porto, Portugal",
• pages = "35--50",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-30668-1",
• DOI = "doi:10.1007/978-3-319-30668-1_3",
• abstract = "Streaming data scenarios introduce a set of requirements that do not exist under supervised learning paradigms typically employed for classification. Specific examples include, anytime operation, non-stationary processes, and limited label budgets. From the perspective of class imbalance, this implies that it is not even possible to guarantee that all classes are present in the samples of data used to construct a model. Moreover, when decisions are made regarding what subset of data to sample, no label information is available. Only after sampling is label information provided. This represents a more challenging task than encountered under non-streaming (offline) scenarios because the training partition contains label information. In this work, we investigate the utility of different protocols for sampling from the stream under the above constraints. Adopting a uniform sampling protocol was previously shown to be reasonably effective under both evolutionary and non-evolutionary streaming classifiers. In this work, we introduce a scheme for using the current champion classifier to bias the sampling of training instances \textit{during} the course of the stream. The resulting streaming framework for genetic programming is more effective at sampling minor classes and therefore reacting to changes in the underlying process responsible for generating the data stream.",
• notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016",

}

Genetic Programming entries for Sara Khanchi Malcolm Heywood Nur Zincir-Heywood

Citations