Evolutionary Undersampling for Extremely Imbalanced Big Data Classification under Apache Spark Triguero, Isaac Galar, Mikel Merino, D. Maillo Hidalgo, Jesús Bustince, Humberto Herrera Triguero, Francisco This work was supported by the Research Projects TIN2011-28488, TIN2013-40765-P, P10-TIC-6858 and P11-TIC-7765. I. Triguero holds a BOF postdoctoral fellowship from the Ghent University. The classification of datasets with a skewed class distribution is an important problem in data mining. Evolutionary undersampling of the majority class has proved to be a successful approach to tackle this issue. Such a challenging task may become even more difficult when the number of the majority class examples is very big. In this scenario, the use of the evolutionary model becomes unpractical due to the memory and time constrictions. Divide-and-conquer approaches based on the MapReduce paradigm have already been proposed to handle this type of problems by dividing data into multiple subsets. However, in extremely imbalanced cases, these models may suffer from a lack of density from the minority class in the subsets considered. Aiming at addressing this problem, in this contribution we provide a new big data scheme based on the new emerging technology Apache Spark to tackle highly imbalanced datasets. We take advantage of its in-memory operations to diminish the effect of the small sample size. The key point of this proposal lies in the independent management of majority and minority class examples, allowing us to keep a higher number of minority class examples in each subset. In our experiments, we analyze the proposed model with several data sets with up to 17 million instances. The results show the goodness of this evolutionary undersampling model for extremely imbalanced big data classification. 2020-12-23T12:50:24Z 2020-12-23T12:50:24Z 2016 info:eu-repo/semantics/conferenceObject Published version: I. Triguero, M. Galar, D. Merino, J. Maillo, H. Bustince and F. Herrera, "Evolutionary undersampling for extremely imbalanced big data classification under apache spark," 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, 2016, pp. 640-647, [doi: 10.1109/CEC.2016.7743853.] http://hdl.handle.net/10481/65146 10.1109/CEC.2016.7743853 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España IEEE