Repository landing page

We are not able to resolve this OAI Identifier to the repository landing page. If you are the repository manager for this record, please head to the Dashboard and adjust the settings.

Evolutionary Undersampling for Extremely Imbalanced Big Data Classification under Apache Spark

Abstract

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.TIN2011-28488TIN2013-40765-PP10-TIC-6858P11-TIC-776

Similar works

Full text

thumbnail-image

Repositorio Institucional Universidad de Granada

redirect
Last time updated on 25/12/2020

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.