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Fuzzy and smote resampling technique for imbalanced data sets


Zorkeflee, Maisarah and Mohamed Din, Aniza and Ku-Mahamud, Ku Ruhana (2015) Fuzzy and smote resampling technique for imbalanced data sets. In: 5th International Conference on Computing and Informatics (ICOCI) 2015, 11-13 August 2015, Istanbul, Turkey.

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Abstract

There are many factors that could affect the performance of a classifier.One of these factors is having imbalanced datasets which could lead to problem in classification accuracy.In binary classification, classifier often ignores instances in minority class.Resampling technique, specifically, undersampling and oversampling are the techniques that are commonly used to overcome the problem related to imbalanced data sets. In this study, an integration of undersampling and oversampling techniques is proposed to improve classification accuracy.The proposed technique is an integration between Fuzzy Distance-based Undersampling and SMOTE.The findings from the study indicate that the proposed combination technique is able to produce more balanced datasets to improve the classification accuracy.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN No: 978-967-0910-02-4 Jointly organized by: Universiti Utara Malaysia & Istanbul Zaim University
Uncontrolled Keywords: imbalanced data, fuzzy logic, fuzzy distance-based undersampling, SMOTE
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Mrs. Aniza Mohamed Din
Date Deposited: 12 Oct 2015 07:42
Last Modified: 28 Apr 2016 02:17
URI: http://repo.uum.edu.my/id/eprint/15646

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