Evolutionary undersampling for extremely imbalanced big data classification under apache spark

Triguero, Isaac and Galar, M. and Merino, D. and Maillo, Jesus and Bustince, H. and Herrera, Francisco (2016) Evolutionary undersampling for extremely imbalanced big data classification under apache spark. In: 2016 IEEE Congress on Evolutionary Computation (CEC), 24-29 July, 2016, Vancouver, Canada.

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview

Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Big data, Sparks, Data mining, Data models, Biological cells, Proposals, Standards
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Eprints, Support
Date Deposited: 22 Nov 2016 09:54
Last Modified: 23 Nov 2016 17:27
URI: http://eprints.nottingham.ac.uk/id/eprint/38876

Actions (Archive Staff Only)

Edit View Edit View