Oversampling imbalanced data in the string space

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Title: Oversampling imbalanced data in the string space
Authors: Castellanos, Francisco J. | Valero Mas, José Javier | Calvo-Zaragoza, Jorge | Rico Juan, Juan Ramón
Research Group/s: Reconocimiento de Formas e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Class imbalance problem | Oversampling | String space | SMOTE
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 1-Feb-2018
Publisher: Elsevier
Citation: Pattern Recognition Letters. 2018, 103: 32-38. doi:10.1016/j.patrec.2018.01.003
Abstract: Imbalanced data is a typical problem in the supervised classification field, which occurs when the different classes are not equally represented. This fact typically results in the classifier biasing its performance towards the class representing the majority of the elements. Many methods have been proposed to alleviate this scenario, yet all of them assume that data is represented as feature vectors. In this paper we propose a strategy to balance a dataset whose samples are encoded as strings. Our approach is based on adapting the well-known Synthetic Minority Over-sampling Technique (SMOTE) algorithm to the string space. More precisely, data generation is achieved with an iterative approach to create artificial strings within the segment between two given samples of the training set. Results with several datasets and imbalance ratios show that the proposed strategy properly deals with the problem in all cases considered.
Sponsor: This work was partially supported by the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013- 48152-C2-1-R supported by EU FEDER funds), the Universidad de Alicante through the FPU program (UAFPU2014–5883) and grant GRE-16-04 .
URI: http://hdl.handle.net/10045/72581
ISSN: 0167-8655 (Print) | 1872-7344 (Online)
DOI: 10.1016/j.patrec.2018.01.003
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Elsevier B.V.
Peer Review: si
Publisher version: http://dx.doi.org/10.1016/j.patrec.2018.01.003
Appears in Collections:INV - GRFIA - Artículos de Revistas

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