Fuzzy and smote resampling technique for imbalanced data sets

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, unders...

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Main Authors: Zorkeflee, Maisarah, Mohamed Din, Aniza, Ku-Mahamud, Ku Ruhana
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
Online Access:https://repo.uum.edu.my/id/eprint/15646/1/PID160.pdf
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author Zorkeflee, Maisarah
Mohamed Din, Aniza
Ku-Mahamud, Ku Ruhana
author_facet Zorkeflee, Maisarah
Mohamed Din, Aniza
Ku-Mahamud, Ku Ruhana
author_sort Zorkeflee, Maisarah
collection UUM
description 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.
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spelling uum-156462016-04-28T02:17:22Z https://repo.uum.edu.my/id/eprint/15646/ Fuzzy and smote resampling technique for imbalanced data sets Zorkeflee, Maisarah Mohamed Din, Aniza Ku-Mahamud, Ku Ruhana QA75 Electronic computers. Computer science 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. 2015-08-11 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/15646/1/PID160.pdf 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. http://www.icoci.cms.net.my/proceedings/2015/TOC.html
spellingShingle QA75 Electronic computers. Computer science
Zorkeflee, Maisarah
Mohamed Din, Aniza
Ku-Mahamud, Ku Ruhana
Fuzzy and smote resampling technique for imbalanced data sets
title Fuzzy and smote resampling technique for imbalanced data sets
title_full Fuzzy and smote resampling technique for imbalanced data sets
title_fullStr Fuzzy and smote resampling technique for imbalanced data sets
title_full_unstemmed Fuzzy and smote resampling technique for imbalanced data sets
title_short Fuzzy and smote resampling technique for imbalanced data sets
title_sort fuzzy and smote resampling technique for imbalanced data sets
topic QA75 Electronic computers. Computer science
url https://repo.uum.edu.my/id/eprint/15646/1/PID160.pdf
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