An enhanced resampling technique for imbalanced data sets

A data set is considered imbalanced if the distribution of instances in one class (majority class) outnumbers the other class (minority class). The main problem related to binary imbalanced data sets is classifiers tend to ignore the minority class. Numerous resampling techniques such as undersampl...

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Main Author:	Maisarah, Zorkeflee
Format:	Thesis
Language:	eng eng
Published:	2015
Subjects:	QA76.76 Fuzzy System.
Online Access:	https://etd.uum.edu.my/5330/1/s814594.pdf https://etd.uum.edu.my/5330/2/s814594_abstract.pdf
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id	my-uum-etd.5330
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Mohamed Din, Aniza Ku Mahamud, Ku Ruhana
topic	QA76.76 Fuzzy System.
spellingShingle	QA76.76 Fuzzy System. Maisarah, Zorkeflee An enhanced resampling technique for imbalanced data sets
description	A data set is considered imbalanced if the distribution of instances in one class (majority class) outnumbers the other class (minority class). The main problem related to binary imbalanced data sets is classifiers tend to ignore the minority class. Numerous resampling techniques such as undersampling, oversampling, and a combination of both techniques have been widely used. However, the undersampling and oversampling techniques suffer from elimination and addition of relevant data which may lead to poor classification results. Hence, this study aims to increase classification metrics by enhancing the undersampling technique and combining it with an existing oversampling technique. To achieve this objective, a Fuzzy Distancebased Undersampling (FDUS) is proposed. Entropy estimation is used to produce fuzzy thresholds to categorise the instances in majority and minority class into membership functions. FDUS is then combined with the Synthetic Minority Oversampling TEchnique (SMOTE) known as FDUS+SMOTE, which is executed in sequence until a balanced data set is achieved. FDUS and FDUS+SMOTE are compared with four techniques based on classification accuracy, F-measure and Gmean. From the results, FDUS achieved better classification accuracy, F-measure and G-mean, compared to the other techniques with an average of 80.57%, 0.85 and 0.78, respectively. This showed that fuzzy logic when incorporated with Distance-based Undersampling technique was able to reduce the elimination of relevant data. Further, the findings showed that FDUS+SMOTE performed better than combination of SMOTE and Tomek Links, and SMOTE and Edited Nearest Neighbour on benchmark data sets. FDUS+SMOTE has minimised the removal of relevant data from the majority class and avoid overfitting. On average, FDUS and FDUS+SMOTE were able to balance categorical, integer and real data sets and enhanced the performance of binary classification. Furthermore, the techniques performed well on small record size data sets that have of instances in the range of approximately 100 to 800.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Maisarah, Zorkeflee
author_facet	Maisarah, Zorkeflee
author_sort	Maisarah, Zorkeflee
title	An enhanced resampling technique for imbalanced data sets
title_short	An enhanced resampling technique for imbalanced data sets
title_full	An enhanced resampling technique for imbalanced data sets
title_fullStr	An enhanced resampling technique for imbalanced data sets
title_full_unstemmed	An enhanced resampling technique for imbalanced data sets
title_sort	enhanced resampling technique for imbalanced data sets
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2015
url	https://etd.uum.edu.my/5330/1/s814594.pdf https://etd.uum.edu.my/5330/2/s814594_abstract.pdf
_version_	1747827910085967872
spelling	my-uum-etd.53302021-04-04T07:31:37Z An enhanced resampling technique for imbalanced data sets 2015 Maisarah, Zorkeflee Mohamed Din, Aniza Ku Mahamud, Ku Ruhana Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA76.76 Fuzzy System. A data set is considered imbalanced if the distribution of instances in one class (majority class) outnumbers the other class (minority class). The main problem related to binary imbalanced data sets is classifiers tend to ignore the minority class. Numerous resampling techniques such as undersampling, oversampling, and a combination of both techniques have been widely used. However, the undersampling and oversampling techniques suffer from elimination and addition of relevant data which may lead to poor classification results. Hence, this study aims to increase classification metrics by enhancing the undersampling technique and combining it with an existing oversampling technique. To achieve this objective, a Fuzzy Distancebased Undersampling (FDUS) is proposed. Entropy estimation is used to produce fuzzy thresholds to categorise the instances in majority and minority class into membership functions. FDUS is then combined with the Synthetic Minority Oversampling TEchnique (SMOTE) known as FDUS+SMOTE, which is executed in sequence until a balanced data set is achieved. FDUS and FDUS+SMOTE are compared with four techniques based on classification accuracy, F-measure and Gmean. From the results, FDUS achieved better classification accuracy, F-measure and G-mean, compared to the other techniques with an average of 80.57%, 0.85 and 0.78, respectively. This showed that fuzzy logic when incorporated with Distance-based Undersampling technique was able to reduce the elimination of relevant data. Further, the findings showed that FDUS+SMOTE performed better than combination of SMOTE and Tomek Links, and SMOTE and Edited Nearest Neighbour on benchmark data sets. FDUS+SMOTE has minimised the removal of relevant data from the majority class and avoid overfitting. On average, FDUS and FDUS+SMOTE were able to balance categorical, integer and real data sets and enhanced the performance of binary classification. Furthermore, the techniques performed well on small record size data sets that have of instances in the range of approximately 100 to 800. 2015 Thesis https://etd.uum.edu.my/5330/ https://etd.uum.edu.my/5330/1/s814594.pdf text eng public https://etd.uum.edu.my/5330/2/s814594_abstract.pdf text eng public masters masters Universiti Utara Malaysia Alejo, R., Garcia, V., Sotoca, J. M., Mollineda, R. A., & Sanchez, J. S. (2007). Improving the performance of the RBF neural networks trained with imbalanced samples. Computational and Ambient Intelligence, 4507, 162–169. Anand, A., Pugalenthi, G., Fogel, G. B., & Suganthan, P. N. (2010). An approach for classification of highly imbalanced data using weighting and undersampling. 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