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...
| Κύριοι συγγραφείς: | , , |
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| Μορφή: | Conference or Workshop Item |
| Γλώσσα: | English |
| Έκδοση: |
2015
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| Θέματα: | |
| Διαθέσιμο Online: | https://repo.uum.edu.my/id/eprint/15646/1/PID160.pdf |
| _version_ | 1825803570301108224 |
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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. |
| first_indexed | 2024-07-04T05:59:16Z |
| format | Conference or Workshop Item |
| id | uum-15646 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:59:16Z |
| publishDate | 2015 |
| record_format | eprints |
| 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 |
| work_keys_str_mv | AT zorkefleemaisarah fuzzyandsmoteresamplingtechniqueforimbalanceddatasets AT mohameddinaniza fuzzyandsmoteresamplingtechniqueforimbalanceddatasets AT kumahamudkuruhana fuzzyandsmoteresamplingtechniqueforimbalanceddatasets |